Merge branch 'upstream' into concedo_experimental

# Conflicts:
#	docs/backend/SYCL.md
#	docs/backend/snapdragon/developer.md
#	examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp
#	examples/sycl/run-llama2.sh
#	examples/sycl/start-svr.sh
#	examples/sycl/test.sh
#	examples/sycl/win-run-llama2.bat
#	examples/sycl/win-start-svr.bat
#	examples/sycl/win-test.bat
#	ggml/CMakeLists.txt
#	ggml/src/ggml-et/et-kernels/src/ssm_scan_f32.c
#	ggml/src/ggml-et/ggml-et-ops.cpp
#	ggml/src/ggml-et/ggml-et-ops.h
#	ggml/src/ggml-sycl/ssm_scan.cpp
#	ggml/src/ggml-webgpu/ggml-webgpu.cpp
#	ggml/src/ggml-webgpu/wgsl-shaders/ssm_scan.wgsl
#	scripts/bench-models.sh
#	scripts/snapdragon/adb/run-bench.sh
#	scripts/snapdragon/adb/run-cli.sh
#	scripts/snapdragon/adb/run-completion.sh
#	scripts/snapdragon/adb/run-mtmd.sh
#	scripts/snapdragon/windows/run-bench.ps1
#	scripts/snapdragon/windows/run-cli.ps1
#	scripts/snapdragon/windows/run-completion.ps1
#	scripts/snapdragon/windows/run-mtmd.ps1
#	scripts/sync-ggml.last
#	scripts/sync_vendor.py
#	tests/CMakeLists.txt
#	tests/test-backend-ops.cpp
#	tests/test-chat.cpp
#	tests/test-jinja.cpp
#	tests/test-llama-archs.cpp
#	tools/cli/README.md
#	tools/completion/README.md
#	tools/llama-bench/README.md
#	tools/server/README.md

.github/workflows/make-release.yml

@@ -0,0 +1,46 @@
+name: Make Release
+
+on:
+  workflow_dispatch:
+    inputs:
+      dry_run:
+        description: 'Dry run - validate without creating the tag'
+        required: true
+        type: boolean
+        default: true
+
+env:
+  GH_TOKEN: ${{ github.token }}
+
+permissions:
+  contents: write
+
+jobs:
+  make-release:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v6
+
+      - name: Run release checks
+        id: checks
+        run: bash scripts/make-release-checks.sh ${{ github.event.inputs.dry_run == 'true' && '--dry-run' || '' }}
+        env:
+          GITHUB_REPOSITORY: ${{ github.repository }}
+
+      - name: Create release tag
+        if: ${{ github.event.inputs.dry_run == 'false' }}
+        run: |
+          VERSION="${{ steps.checks.outputs.version }}"
+          git config user.name "github-actions[bot]"
+          git config user.email "github-actions[bot]@users.noreply.github.com"
+          git tag -a "${VERSION}" -m "Release ${VERSION}"
+          git push origin "${VERSION}"
+          echo "Created and pushed tag ${VERSION}"
+
+      - name: Dry run summary
+        if: ${{ github.event.inputs.dry_run == 'true' }}
+        run: |
+          echo "Dry run complete - all checks passed."
+          echo "Would have created tag: ${{ steps.checks.outputs.version }}"

CMakeLists.txt

@@ -478,6 +478,8 @@ add_library(common2
             tools/mtmd/mtmd-helper.cpp
             tools/mtmd/mtmd-image.cpp
             tools/mtmd/mtmd-helper.h
+            tools/mtmd/mtmd-helper-gen.cpp
+            tools/mtmd/mtmd-helper-common.h
             tools/mtmd/clip.cpp
             tools/mtmd/clip.h
             src/unicode.h

Makefile

@@ -55,8 +55,8 @@ ifdef KCPP_SANITIZE
 CFLAGS += -fsanitize=undefined -fsanitize-undefined-trap-on-error
 CXXFLAGS += -fsanitize=undefined -fsanitize-undefined-trap-on-error
 endif
-CFLAGS   += -I. -Iggml/include -Iggml/src -Iggml/src/ggml-cpu -Iinclude -Isrc -I./common -I./vendor -I./vendor/stb -I./include -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/ttscpp/include -I./otherarch/ttscpp/src -I./otherarch/qwen3tts -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -fno-finite-math-only -std=c11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -DGGML_USE_CPU -DGGML_USE_CPU_REPACK -DGGML_USE_RPC
-CXXFLAGS += -I. -Iggml/include -Iggml/src -Iggml/src/ggml-cpu -Iinclude -Isrc -I./common -I./vendor -I./vendor/stb -I./include -I./otherarch -I./otherarch/tools -I./otherarch/sdcpp -I./otherarch/ttscpp/include -I./otherarch/ttscpp/src -I./otherarch/qwen3tts -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -fno-finite-math-only -std=c++17 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -DGGML_USE_CPU -DGGML_USE_CPU_REPACK -DGGML_USE_RPC
+CFLAGS   += -I. -Iggml/include -Iggml/src -Iggml/src/ggml-cpu -Iinclude -Isrc -I./common -I./vendor -I./vendor/stb -I./include -I./otherarch -I./otherarch/tools -I./tools/mtmd -I./otherarch/sdcpp -I./otherarch/ttscpp/include -I./otherarch/ttscpp/src -I./otherarch/qwen3tts -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -fno-finite-math-only -std=c11 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -DGGML_USE_CPU -DGGML_USE_CPU_REPACK -DGGML_USE_RPC
+CXXFLAGS += -I. -Iggml/include -Iggml/src -Iggml/src/ggml-cpu -Iinclude -Isrc -I./common -I./vendor -I./vendor/stb -I./include -I./otherarch -I./otherarch/tools -I./tools/mtmd -I./otherarch/sdcpp -I./otherarch/ttscpp/include -I./otherarch/ttscpp/src -I./otherarch/qwen3tts -I./otherarch/sdcpp/thirdparty -I./include/vulkan -O3 -fno-finite-math-only -std=c++17 -fPIC -DLOG_DISABLE_LOGS -D_GNU_SOURCE -DGGML_USE_CPU -DGGML_USE_CPU_REPACK -DGGML_USE_RPC
 
 ifndef KCPP_DEBUG
 CFLAGS += -DNDEBUG -s
@@ -579,6 +579,8 @@ mtmd.o: tools/mtmd/mtmd.cpp tools/mtmd/mtmd.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 mtmd-helper.o: tools/mtmd/mtmd-helper.cpp tools/mtmd/mtmd-helper.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
+mtmd-helper-gen.o: tools/mtmd/mtmd-helper-gen.cpp tools/mtmd/mtmd-helper-common.h
+	$(CXX) $(CXXFLAGS) -c $< -o $@
 mtmd-image.o: tools/mtmd/mtmd-image.cpp tools/mtmd/mtmd-image.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 unicode-common.o: common/unicode.cpp common/unicode.h
@@ -667,7 +669,7 @@ ggml-vulkan-shaders-noext.o: ggml/src/ggml-vulkan-shaders-noext.cpp ggml/include
 	$(CXX) $(CXXFLAGS) $(VKGEN_NOEXT_FORCE) $(VULKAN_FLAGS) -c $< -o $@
 
 # intermediate objects
-llama.o: src/llama.cpp ggml/include/ggml.h ggml/include/ggml-alloc.h ggml/include/ggml-backend.h ggml/include/ggml-cuda.h ggml/include/ggml-metal.h include/llama.h otherarch/llama-util.h src/llama-chat.cpp src/llama-mmap.cpp src/llama-context.cpp src/llama-adapter.cpp src/llama-arch.cpp src/llama-batch.cpp src/llama-vocab.cpp src/llama-grammar.cpp src/llama-sampler.cpp src/llama-kv-cache.cpp src/llama-kv-cache-dsa.cpp src/llama-kv-cache-dsv4.cpp src/llama-kv-cache-iswa.cpp src/llama-memory-hybrid.cpp src/llama-memory-hybrid-iswa.cpp src/llama-memory-recurrent.cpp src/llama-model-loader.cpp src/llama-model-saver.cpp src/llama-quant.cpp src/llama-hparams.cpp src/llama-graph.cpp src/llama-io.cpp src/llama-memory.cpp common/fit.cpp ggml/include/ggml.h ggml/include/ggml-cpu.h ggml/include/ggml-cuda.h include/llama.h otherarch/llama-util.h
+llama.o: src/llama.cpp ggml/include/ggml.h ggml/include/ggml-alloc.h ggml/include/ggml-backend.h ggml/include/ggml-cuda.h ggml/include/ggml-metal.h include/llama.h otherarch/llama-util.h src/llama-chat.cpp src/llama-mmap.cpp src/llama-context.cpp src/llama-adapter.cpp src/llama-arch.cpp src/llama-batch.cpp src/llama-vocab.cpp src/llama-grammar.cpp src/llama-sampler.cpp src/llama-kv-cache.cpp src/llama-kv-cache-dsa.cpp src/llama-kv-cache-dsv4.cpp src/llama-kv-cache-iswa.cpp src/llama-kv-cache-msa.cpp src/llama-memory-hybrid.cpp src/llama-memory-hybrid-iswa.cpp src/llama-memory-recurrent.cpp src/llama-model-loader.cpp src/llama-model-saver.cpp src/llama-quant.cpp src/llama-hparams.cpp src/llama-graph.cpp src/llama-io.cpp src/llama-memory.cpp common/fit.cpp ggml/include/ggml.h ggml/include/ggml-cpu.h ggml/include/ggml-cuda.h include/llama.h otherarch/llama-util.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 llama-model.o: src/llama-model.cpp src/llama-model.h src/models/models.h ggml/include/ggml.h include/llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
@@ -688,7 +690,7 @@ budget.o: common/reasoning-budget.cpp common/reasoning-budget.h
 chat.o: common/chat.cpp common/chat.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/backend_fit.cpp src/core/backend_fit.h src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/layer_split_partition.cpp src/core/layer_split_partition.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/detailer.cpp src/detailer.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/ip_adapter.hpp src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/detector/yolov8.h src/model/diffusion/anima.hpp src/model/diffusion/animatediff.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/hunyuan.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/lingbot_video.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mage_flow.hpp src/model/diffusion/minit2i.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/sefi_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/kcpp_sdcpp_quantized_safetensors.hpp src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/streaming_writer.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/hunyuan_vae.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/mage_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/imatrix.cpp src/runtime/imatrix.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
+SDCPP_COMMON_BASENAMES := include/stable-diffusion.h src/conditioning/conditioner.hpp src/core/backend_fit.cpp src/core/backend_fit.h src/core/ggml_extend_backend.cpp src/core/ggml_extend_backend.h src/core/ggml_extend.hpp src/core/ggml_graph_cut.cpp src/core/ggml_graph_cut.h src/core/layer_split_partition.cpp src/core/layer_split_partition.h src/core/ordered_map.hpp src/core/rng.hpp src/core/rng_mt19937.hpp src/core/rng_philox.hpp src/core/tensor_ggml.hpp src/core/tensor.hpp src/core/util.cpp src/core/util.h src/detailer.cpp src/detailer.h src/extensions/generation_extension.h src/extensions/photomaker_extension.cpp src/extensions/pulid_extension.cpp src/kcpp_sd_extensions.h src/model/adapter/ip_adapter.hpp src/model/adapter/lora.hpp src/model/adapter/pmid.hpp src/model/adapter/pulid.hpp src/model/common/block.hpp src/model/common/rope.hpp src/model/detector/yolov8.h src/model/diffusion/anima.hpp src/model/diffusion/animatediff.hpp src/model/diffusion/boogu.hpp src/model/diffusion/control.hpp src/model/diffusion/dit.hpp src/model/diffusion/ernie_image.hpp src/model/diffusion/flux.hpp src/model/diffusion/hidream_o1.hpp src/model/diffusion/hunyuan.hpp src/model/diffusion/ideogram4.hpp src/model/diffusion/krea2.hpp src/model/diffusion/lens.hpp src/model/diffusion/lingbot_video.hpp src/model/diffusion/ltxv.hpp src/model/diffusion/mage_flow.hpp src/model/diffusion/minimax_h3.hpp src/model/diffusion/minit2i.hpp src/model/diffusion/mmdit.hpp src/model/diffusion/model.hpp src/model/diffusion/pid.hpp src/model/diffusion/qwen_image.hpp src/model/diffusion/sefi_image.hpp src/model/diffusion/unet.hpp src/model/diffusion/wan.hpp src/model/diffusion/z_image.hpp src/model.h src/model_io/binary_io.h src/model_io/gguf_io.cpp src/model_io/gguf_io.h src/model_io/gguf_reader_ext.h src/model_io/kcpp_sdcpp_quantized_safetensors.hpp src/model_io/pickle_io.cpp src/model_io/pickle_io.h src/model_io/safetensors_io.cpp src/model_io/safetensors_io.h src/model_io/streaming_writer.h src/model_io/tensor_storage.h src/model_io/torch_legacy_io.cpp src/model_io/torch_legacy_io.h src/model_io/torch_zip_io.cpp src/model_io/torch_zip_io.h src/model_loader.cpp src/model_loader.h src/model_manager.cpp src/model_manager.h src/model/te/clip.hpp src/model/te/llm.hpp src/model/te/t5.hpp src/model/upscaler/esrgan.hpp src/model/upscaler/ltx_latent_upscaler.hpp src/model/vae/audio_vae.hpp src/model/vae/auto_encoder_kl.hpp src/model/vae/hunyuan_vae.hpp src/model/vae/ltx_audio_vae.hpp src/model/vae/ltx_vae.hpp src/model/vae/mage_vae.hpp src/model/vae/minimax_h3_audio_vae.hpp src/model/vae/minimax_h3_vae.hpp src/model/vae/tae.hpp src/model/vae/vae.hpp src/model/vae/wan_vae.hpp src/name_conversion.cpp src/name_conversion.h src/runtime/cache_dit.hpp src/runtime/condition_cache_utils.hpp src/runtime/denoiser.hpp src/runtime/easycache.hpp src/runtime/gits_noise.h src/runtime/guidance.cpp src/runtime/guidance.h src/runtime/imatrix.cpp src/runtime/imatrix.h src/runtime/latent-preview.h src/runtime/preprocessing.hpp src/runtime/sample-cache.cpp src/runtime/sample-cache.h src/runtime/spectrum.hpp src/runtime/ucache.hpp src/stable-diffusion.cpp src/tokenizers/bpe_tokenizer.cpp src/tokenizers/bpe_tokenizer.h src/tokenizers/clip_tokenizer.cpp src/tokenizers/clip_tokenizer.h src/tokenizers/gemma_tokenizer.cpp src/tokenizers/gemma_tokenizer.h src/tokenizers/gpt_oss_tokenizer.cpp src/tokenizers/gpt_oss_tokenizer.h src/tokenizers/mistral_tokenizer.cpp src/tokenizers/mistral_tokenizer.h src/tokenizers/qwen2_tokenizer.cpp src/tokenizers/qwen2_tokenizer.h src/tokenizers/t5_unigram_tokenizer.cpp src/tokenizers/t5_unigram_tokenizer.h src/tokenizers/tokenizer.cpp src/tokenizers/tokenizer.h src/tokenizers/tokenize_util.cpp src/tokenizers/tokenize_util.h src/tokenizers/vocab/vocab.h src/upscaler.cpp src/upscaler.h src/weight_manager.h
 
 SDCPP_MAIN_BASENAMES := examples/cli/image_metadata.cpp examples/cli/image_metadata.h examples/cli/main.cpp examples/cli/msf_gif.h examples/common/common.cpp examples/common/common.h examples/common/log.cpp examples/common/log.h examples/common/media_io.cpp examples/common/media_io.h examples/common/resource_owners.hpp src/tokenizers/vocab/clip_merges.hpp src/tokenizers/vocab/gemma2_merges.hpp src/tokenizers/vocab/gemma2_vocab.hpp src/tokenizers/vocab/gemma_merges.hpp src/tokenizers/vocab/gemma_vocab.hpp src/tokenizers/vocab/gpt_oss_merges.hpp src/tokenizers/vocab/gpt_oss_vocab.hpp src/tokenizers/vocab/mistral_merges.hpp src/tokenizers/vocab/mistral_vocab.hpp src/tokenizers/vocab/qwen_merges.hpp src/tokenizers/vocab/t5.hpp src/tokenizers/vocab/umt5.hpp src/tokenizers/vocab/vocab.cpp src/convert.cpp src/version.cpp
 
@@ -764,35 +766,35 @@ clean:
 	rm -vf otherarch/sdcpp/*.o otherarch/sdcpp/*/*.o otherarch/sdcpp/*/*/*.o otherarch/sdcpp/*/*/*/*.o
 
 # useful tools
-main: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+main: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-mainvk: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+mainvk: tools/completion/main.cpp tools/completion/completion.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-fitparams: tools/fit-params/main.cpp tools/fit-params/fit-params.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+fitparams: tools/fit-params/main.cpp tools/fit-params/fit-params.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-sdmain: $(OBJS_SDCOMMON) $(OBJS_SDMAIN) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+sdmain: $(OBJS_SDCOMMON) $(OBJS_SDMAIN) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-whispermain: otherarch/whispercpp/main.cpp otherarch/whispercpp/whisper.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+whispermain: otherarch/whispercpp/main.cpp otherarch/whispercpp/whisper.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-ttsmain: tools/tts/tts.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+ttsmain: tools/tts/tts.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-gguf-split: tools/gguf-split/gguf-split.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o build-info.h clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+gguf-split: tools/gguf-split/gguf-split.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o build-info.h clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-mtmd-cli: tools/mtmd/mtmd-cli.cpp tools/mtmd/clip.cpp common/debug.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h mtmd.o mtmd-helper.o mtmd-image.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+mtmd-cli: tools/mtmd/mtmd-cli.cpp tools/mtmd/clip.cpp common/debug.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-embedding: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+embedding: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-embeddingvk: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+embeddingvk: examples/embedding/embedding.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) src/llama-cparams.cpp build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-ttscppmain: otherarch/ttscpp/cli/cli.cpp otherarch/ttscpp/cli/playback.cpp otherarch/ttscpp/cli/playback.h otherarch/ttscpp/cli/write_file.cpp otherarch/ttscpp/cli/write_file.h otherarch/ttscpp/cli/vad.cpp otherarch/ttscpp/cli/vad.h otherarch/ttscpp/src/ttscpp.cpp otherarch/ttscpp/src/ttstokenizer.cpp otherarch/ttscpp/src/ttssampler.cpp otherarch/ttscpp/src/parler_model.cpp otherarch/ttscpp/src/dac_model.cpp otherarch/ttscpp/src/ttsutil.cpp otherarch/ttscpp/src/ttsargs.cpp otherarch/ttscpp/src/ttst5_encoder_model.cpp otherarch/ttscpp/src/phonemizer.cpp otherarch/ttscpp/src/tts_model.cpp otherarch/ttscpp/src/kokoro_model.cpp otherarch/ttscpp/src/dia_model.cpp otherarch/ttscpp/src/orpheus_model.cpp otherarch/ttscpp/src/snac_model.cpp otherarch/ttscpp/src/general_neural_audio_codec.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+ttscppmain: otherarch/ttscpp/cli/cli.cpp otherarch/ttscpp/cli/playback.cpp otherarch/ttscpp/cli/playback.h otherarch/ttscpp/cli/write_file.cpp otherarch/ttscpp/cli/write_file.h otherarch/ttscpp/cli/vad.cpp otherarch/ttscpp/cli/vad.h otherarch/ttscpp/src/ttscpp.cpp otherarch/ttscpp/src/ttstokenizer.cpp otherarch/ttscpp/src/ttssampler.cpp otherarch/ttscpp/src/parler_model.cpp otherarch/ttscpp/src/dac_model.cpp otherarch/ttscpp/src/ttsutil.cpp otherarch/ttscpp/src/ttsargs.cpp otherarch/ttscpp/src/ttst5_encoder_model.cpp otherarch/ttscpp/src/phonemizer.cpp otherarch/ttscpp/src/tts_model.cpp otherarch/ttscpp/src/kokoro_model.cpp otherarch/ttscpp/src/dia_model.cpp otherarch/ttscpp/src/orpheus_model.cpp otherarch/ttscpp/src/snac_model.cpp otherarch/ttscpp/src/general_neural_audio_codec.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-qwen3tts: otherarch/qwen3tts/q3ttsmain.cpp otherarch/qwen3tts/qwen3_tts.cpp otherarch/qwen3tts/text_tokenizer.cpp otherarch/qwen3tts/gguf_loader.cpp otherarch/qwen3tts/tts_transformer.cpp otherarch/qwen3tts/audio_tokenizer_decoder.cpp otherarch/qwen3tts/audio_tokenizer_encoder.cpp otherarch/qwen3tts/coreml_code_predictor_stub.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+qwen3tts: otherarch/qwen3tts/q3ttsmain.cpp otherarch/qwen3tts/qwen3_tts.cpp otherarch/qwen3tts/text_tokenizer.cpp otherarch/qwen3tts/gguf_loader.cpp otherarch/qwen3tts/tts_transformer.cpp otherarch/qwen3tts/audio_tokenizer_decoder.cpp otherarch/qwen3tts/audio_tokenizer_encoder.cpp otherarch/qwen3tts/coreml_code_predictor_stub.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-rpcserver: tools/rpc/rpc-server.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+rpcserver: tools/rpc/rpc-server.cpp common/arg.cpp common/preset.cpp $(COMMON_DOWNLOAD_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-llamaserver: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+llamaserver: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
-llamaservervk: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
+llamaservervk: $(LLAMASERVER_SRCS) $(LLAMASERVER_COMMON_SRCS) build-info.h ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o console.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o ggml-repack.o $(OBJS_FULL) $(OBJS) lib/vulkan-1.lib
 	$(CXX) $(CXXFLAGS) $(LLAMASERVER_CXXFLAGS) -DGGML_USE_VULKAN $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 ggml/src/ggml-vulkan-shaders.cpp: ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
@@ -892,15 +894,18 @@ else
 	@echo 'Vulkan Shaders (no extensions) Rebuilt for Linux...'
 endif
 
+# common object files for all libraries
+KOBOLDCPP_COMMON_OBJS = expose.o chat.o ggml-binops.o ggml-unops.o ggml-backend.o ggml-backend-meta.o ggml-repack.o llama.o llama-model.o embeddings_default.o music_default.o tts_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o $(OBJS) $(OBJS_SDTYPE)
+
 #generated libraries
-koboldcpp_default: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+koboldcpp_default: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml_v3.o ggml_v2.o ggml_v1.o gpttype_adapter.o whispercpp_default.o clip_default.o ggml-backend-reg_default.o $(KOBOLDCPP_COMMON_OBJS) $(OBJS_FULL)
 	$(DEFAULT_BUILD)
 
-koboldcpp_macos_failsafe: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+koboldcpp_macos_failsafe: ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml_v3.o ggml_v2.o ggml_v1.o gpttype_adapter.o whispercpp_default.o clip_default.o ggml-backend-reg_default.o $(KOBOLDCPP_COMMON_OBJS) $(OBJS_FULL)
 	$(DEFAULT_BUILD)
 
 ifdef FAILSAFE_BUILD
-koboldcpp_failsafe: ggml_v4_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml-binops.o ggml-unops.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o gpttype_adapter_failsafe.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FAILSAFE) $(OBJS)
+koboldcpp_failsafe: ggml_v4_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o gpttype_adapter_failsafe.o whispercpp_default.o clip_default.o ggml-backend-reg_default.o $(KOBOLDCPP_COMMON_OBJS) $(OBJS_FAILSAFE)
 	$(FAILSAFE_BUILD)
 else
 koboldcpp_failsafe:
@@ -908,7 +913,7 @@ koboldcpp_failsafe:
 endif
 
 ifdef NOAVX2_BUILD
-koboldcpp_noavx2: ggml_v4_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml-binops.o ggml-unops.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o gpttype_adapter_failsafe.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_default.o tts_default.o music_default.o embeddings_default.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_SIMPLE) $(OBJS)
+koboldcpp_noavx2: ggml_v4_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o gpttype_adapter_failsafe.o whispercpp_default.o clip_default.o ggml-backend-reg_default.o $(KOBOLDCPP_COMMON_OBJS) $(OBJS_SIMPLE)
 	$(NOAVX2_BUILD)
 else
 koboldcpp_noavx2:
@@ -916,7 +921,7 @@ koboldcpp_noavx2:
 endif
 
 ifdef CUBLAS_BUILD
-koboldcpp_cublas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o gpttype_adapter_cublas.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_cublas.o tts_default.o music_default.o embeddings_default.o clip_cublas.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_cublas.o ggml-repack.o $(CUBLAS_OBJS) $(OBJS_FULL) $(OBJS)
+koboldcpp_cublas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o gpttype_adapter_cublas.o whispercpp_cublas.o clip_cublas.o ggml-backend-reg_cublas.o $(CUBLAS_OBJS) $(KOBOLDCPP_COMMON_OBJS) $(OBJS_FULL)
 	$(CUBLAS_BUILD)
 else
 koboldcpp_cublas:
@@ -924,7 +929,7 @@ koboldcpp_cublas:
 endif
 
 ifdef HIPBLAS_BUILD
-koboldcpp_hipblas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o expose.o gpttype_adapter_cublas.o llama.o chat.o llama-model.o $(OBJS_SDTYPE) whispercpp_cublas.o tts_default.o music_default.o embeddings_default.o clip_cublas.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_cublas.o ggml-repack.o $(HIP_OBJS) $(OBJS_FULL) $(OBJS)
+koboldcpp_hipblas: ggml_v4_cublas.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml_v3_cublas.o ggml_v2_cublas.o ggml_v1.o gpttype_adapter_cublas.o whispercpp_cublas.o clip_cublas.o ggml-backend-reg_cublas.o $(HIP_OBJS) $(KOBOLDCPP_COMMON_OBJS) $(OBJS_FULL)
 	$(HIPBLAS_BUILD)
 else
 koboldcpp_hipblas:
@@ -932,12 +937,12 @@ koboldcpp_hipblas:
 endif
 
 ifdef VULKAN_BUILD
-koboldcpp_vulkan: ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o ggml_v3.o ggml_v2.o ggml_v1.o expose.o gpttype_adapter_vulkan.o llama.o chat.o llama-model.o ggml-vulkan.o ggml-vulkan-shaders.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+koboldcpp_vulkan: ggml_v4_vulkan.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml_v3.o ggml_v2.o ggml_v1.o gpttype_adapter_vulkan.o ggml-vulkan.o ggml-vulkan-shaders.o whispercpp_vulkan.o clip_vulkan.o ggml-backend-reg_vulkan.o $(KOBOLDCPP_COMMON_OBJS) $(OBJS_FULL)
 	$(VULKAN_BUILD)
 ifdef NOAVX2_BUILD
-koboldcpp_vulkan_noavx2: ggml_v4_vulkan_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml-binops.o ggml-unops.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o expose.o gpttype_adapter_vulkan_noavx2.o llama.o chat.o llama-model.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_SIMPLE) $(OBJS)
+koboldcpp_vulkan_noavx2: ggml_v4_vulkan_noavx2.o ggml-cpu_v4_noavx2.o ggml-ops-noavx2.o ggml-vec-noavx2.o ggml_v3_noavx2.o ggml_v2_noavx2.o ggml_v1_failsafe.o gpttype_adapter_vulkan_noavx2.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o whispercpp_vulkan.o clip_vulkan.o ggml-backend-reg_vulkan.o $(KOBOLDCPP_COMMON_OBJS) $(OBJS_SIMPLE)
 	$(VULKAN_BUILD)
-koboldcpp_vulkan_failsafe: ggml_v4_vulkan_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml-binops.o ggml-unops.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o expose.o gpttype_adapter_vulkan_noavx2.o llama.o chat.o llama-model.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o $(OBJS_SDTYPE) whispercpp_vulkan.o tts_default.o music_default.o embeddings_default.o clip_vulkan.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_vulkan.o ggml-repack.o $(OBJS_SIMPLER) $(OBJS)
+koboldcpp_vulkan_failsafe: ggml_v4_vulkan_failsafe.o ggml-cpu_v4_failsafe.o ggml-ops-failsafe.o ggml-vec-failsafe.o ggml_v3_failsafe.o ggml_v2_failsafe.o ggml_v1_failsafe.o gpttype_adapter_vulkan_noavx2.o ggml-vulkan-noext.o ggml-vulkan-shaders-noext.o whispercpp_vulkan.o clip_vulkan.o ggml-backend-reg_vulkan.o $(KOBOLDCPP_COMMON_OBJS) $(OBJS_SIMPLER)
 	$(VULKAN_BUILD)
 else
 koboldcpp_vulkan_noavx2:
@@ -955,15 +960,15 @@ koboldcpp_vulkan_failsafe:
 endif
 
 # tools
-quantize_gguf: tools/quantize/main.cpp tools/quantize/quantize.cpp common/imatrix-loader.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_gguf: tools/quantize/main.cpp tools/quantize/quantize.cpp common/imatrix-loader.cpp ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gptj: otherarch/tools/gptj_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_gptj: otherarch/tools/gptj_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_gpt2: otherarch/tools/gpt2_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_gpt2: otherarch/tools/gpt2_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_neox: otherarch/tools/neox_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_neox: otherarch/tools/neox_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
-quantize_mpt: otherarch/tools/mpt_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
+quantize_mpt: otherarch/tools/mpt_quantize.cpp otherarch/tools/common-ggml.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o clip_default.o mtmd.o mtmd-helper.o mtmd-helper-gen.o mtmd-image.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 quantize_ace: otherarch/acestep/quantize-acestep.cpp tools/mtmd/clip.cpp ggml_v3.o ggml.o ggml-cpu.o ggml-ops.o ggml-vec.o ggml-binops.o ggml-unops.o llama.o chat.o llama-model.o ggml-backend.o ggml-backend-meta.o ggml-backend-reg_default.o ggml-repack.o $(OBJS_FULL) $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)

cmake/arm64-windows-msvc-cuda.cmake

@@ -0,0 +1,26 @@
+# Used to cross-compile ggml-cuda for Windows ARM64 on an x64 Windows host.
+set( CMAKE_SYSTEM_NAME Windows )
+set( CMAKE_SYSTEM_PROCESSOR arm64 )
+
+if ( DEFINED CUDAToolkit_ROOT )
+    file( TO_CMAKE_PATH "${CUDAToolkit_ROOT}" CUDA_ROOT )
+elseif ( DEFINED ENV{CUDA_PATH} )
+    file( TO_CMAKE_PATH "$ENV{CUDA_PATH}" CUDA_ROOT )
+else()
+    message( FATAL_ERROR "Set CUDAToolkit_ROOT or CUDA_PATH to a Windows CUDA Toolkit with ARM64 target libraries" )
+endif()
+
+if ( DEFINED ENV{VCToolsInstallDir} )
+    file( TO_CMAKE_PATH "$ENV{VCToolsInstallDir}" MSVC_TOOLS_ROOT )
+    set( CMAKE_CUDA_HOST_COMPILER "${MSVC_TOOLS_ROOT}/bin/Hostx64/arm64/cl.exe" CACHE FILEPATH "" )
+endif()
+
+set( CMAKE_CUDA_COMPILER "${CUDA_ROOT}/bin/nvcc.exe" CACHE FILEPATH "" )
+set( CMAKE_CUDA_FLAGS_INIT "-target-dir=arm64" )
+
+# FindCUDAToolkit selects lib/x64 from the host architecture on Windows.
+set( CUDA_CUDART              "${CUDA_ROOT}/lib/arm64/cudart.lib"   CACHE FILEPATH "" )
+set( CUDA_cudart_LIBRARY      "${CUDA_ROOT}/lib/arm64/cudart.lib"   CACHE FILEPATH "" )
+set( CUDA_cublas_LIBRARY      "${CUDA_ROOT}/lib/arm64/cublas.lib"   CACHE FILEPATH "" )
+set( CUDA_cublasLt_LIBRARY    "${CUDA_ROOT}/lib/arm64/cublasLt.lib" CACHE FILEPATH "" )
+set( CUDA_cuda_driver_LIBRARY "${CUDA_ROOT}/lib/arm64/cuda.lib"     CACHE FILEPATH "" )

common/arg.cpp

@@ -28,6 +28,7 @@
 #include <algorithm>
 #include <cinttypes>
 #include <climits>
+#include <cmath>
 #include <cstdarg>
 #include <filesystem>
 #include <fstream>
@@ -35,6 +36,7 @@
 #include <regex>
 #include <set>
 #include <string>
+#include <system_error>
 #include <thread> // for hardware_concurrency
 #include <vector>
 
@@ -61,6 +63,7 @@ static std::initializer_list<enum llama_example> mmproj_examples = {
     LLAMA_EXAMPLE_MTMD,
     LLAMA_EXAMPLE_SERVER,
     LLAMA_EXAMPLE_CLI,
+    LLAMA_EXAMPLE_TTS,
 };
 
 static std::string read_file(const std::string & fname) {
@@ -360,7 +363,6 @@ static bool spec_types_is_default(const common_params & params) {
 common_models_handler common_models_handler_init(const common_params & params, llama_example curr_ex) {
     common_download_hf_plan plan;
     common_download_hf_plan plan_spec;
-    common_download_hf_plan plan_voc;
     common_download_opts opts;
 
     const bool spec_type_draft_mtp = std::find(params.speculative.types.begin(),
@@ -375,6 +377,10 @@ common_models_handler common_models_handler_init(const common_params & params, l
                                            params.speculative.types.end(),
                                            COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3) != params.speculative.types.end();
 
+    const bool spec_type_draft_dspark = std::find(params.speculative.types.begin(),
+                                           params.speculative.types.end(),
+                                           COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK) != params.speculative.types.end();
+
     // only download mmproj if the current example is using it
     bool use_mmproj = false;
     for (const auto & ex : mmproj_examples) {
@@ -389,6 +395,7 @@ common_models_handler common_models_handler_init(const common_params & params, l
     opts.download_mtp    = spec_type_draft_mtp;
     opts.download_eagle3 = spec_type_draft_eagle3;
     opts.download_dflash = spec_type_draft_dflash;
+    opts.download_dspark = spec_type_draft_dspark;
     opts.download_mmproj = use_mmproj && !params.no_mmproj
                         && params.mmproj.path.empty() && params.mmproj.url.empty();
 
@@ -403,15 +410,12 @@ common_models_handler common_models_handler_init(const common_params & params, l
             opts_spec.download_mtp    = true;
             opts_spec.download_dflash = true;
             opts_spec.download_eagle3 = true;
+            opts_spec.download_dspark = true;
         }
         plan_spec = common_download_get_hf_plan(params.speculative.draft.mparams, opts_spec);
     }
 
-    if (!params.vocoder.model.hf_repo.empty()) {
-        plan_voc = common_download_get_hf_plan(params.vocoder.model, opts);
-    }
-
-    return common_models_handler{plan, plan_spec, plan_voc, opts};
+    return common_models_handler{plan, plan_spec, opts};
 }
 
 bool common_models_handler_is_preset_repo(const common_models_handler & handler) {
@@ -461,7 +465,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
 
     auto & plan      = handler.plan;
     auto & plan_spec = handler.plan_spec;
-    auto & plan_voc  = handler.plan_voc;
 
     auto opts = handler.opts; // copy
     opts.callback = callback;
@@ -476,7 +479,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
     };
     handle_url(params.model);
     handle_url(params.mmproj);
-    handle_url(params.vocoder.model);
     handle_url(params.speculative.draft.mparams);
 
     // optionally, if docker repo is set, resolve it
@@ -504,14 +506,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         task.opts       = opts;
         tasks.push_back(task);
     }
-    if (!params.vocoder.model.url.empty()) {
-        common_download_task task;
-        task.url        = params.vocoder.model.url;
-        task.local_path = params.vocoder.model.path;
-        task.opts       = opts;
-        tasks.push_back(task);
-    }
-
     bool had_spec_url = false;
     if (!params.speculative.draft.mparams.url.empty()) {
         common_download_task task;
@@ -545,12 +539,19 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         plan_spec.mtp    = {};
         plan_spec.dflash = {};
         plan_spec.eagle3 = {};
+        plan_spec.dspark = {};
     }
 
     // infer the speculative type from the sidecar shipped by the draft repo when none is requested
     if (spec_types_is_default(params)) {
         if (!plan_spec.mtp.local_path.empty()) {
             params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_MTP };
+            plan_spec.dspark = {};
+            plan_spec.dflash = {};
+            plan_spec.eagle3 = {};
+        } else if (!plan_spec.dspark.local_path.empty()) {
+            // dspark outranks dflash, its sidecar carries the extra Markov head
+            params.speculative.types = { COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK };
             plan_spec.dflash = {};
             plan_spec.eagle3 = {};
         } else if (!plan_spec.dflash.local_path.empty()) {
@@ -561,10 +562,20 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         }
     }
 
+    // infer the speculative type from the draft GGUF metadata when none is requested
+    // note: reads only the first split - sharded drafts need an explicit --spec-type
+    if (spec_types_is_default(params) && !params.speculative.draft.mparams.path.empty()) {
+        const auto types_gguf = common_speculative_types_from_gguf(params.speculative.draft.mparams.path);
+        if (!types_gguf.empty()) {
+            params.speculative.types = types_gguf;
+        }
+    }
+
     // when a sidecar type is requested, the draft repo resolves to its sidecar instead of a full model
     const bool spec_sidecar_found = !plan_spec.mtp.local_path.empty() ||
                                     !plan_spec.dflash.local_path.empty() ||
-                                    !plan_spec.eagle3.local_path.empty();
+                                    !plan_spec.eagle3.local_path.empty() ||
+                                    !plan_spec.dspark.local_path.empty();
     if (!plan_spec.mtp.local_path.empty() && !had_spec_url) {
         tasks.emplace_back(plan_spec.mtp, opts, [&]() {
             // only use the discovered MTP head when no draft path is set yet
@@ -595,6 +606,16 @@ void common_models_handler_apply(common_models_handler & handler, common_params
             }
         });
     }
+    if (!plan_spec.dspark.local_path.empty() && !had_spec_url) {
+        tasks.emplace_back(plan_spec.dspark, opts, [&]() {
+            // only use the discovered DSpark sidecar when no draft path is set yet
+            if (params.speculative.draft.mparams.path.empty()) {
+                params.speculative.draft.mparams.path = hf_cache::finalize_file(plan_spec.dspark);
+            } else {
+                hf_cache::finalize_file(plan_spec.dspark);
+            }
+        });
+    }
 
     // a wired draft sidecar counts as an explicit draft for the main plan fallback below
     if (spec_sidecar_found) {
@@ -607,11 +628,6 @@ void common_models_handler_apply(common_models_handler & handler, common_params
         had_spec_url = true;
     }
 
-    // handle vocoder plan (e.g. --hf-repo-v)
-    if (!plan_voc.model_files.empty()) {
-        add_tasks(plan_voc.model_files, plan_voc.primary, params.vocoder.model);
-    }
-
     if (!plan.model_files.empty()) {
         add_tasks(plan.model_files, plan.primary, params.model);
     }
@@ -650,6 +666,16 @@ void common_models_handler_apply(common_models_handler & handler, common_params
             }
         });
     }
+    if (!plan.dspark.local_path.empty() && !had_spec_url) {
+        tasks.emplace_back(plan.dspark, opts, [&]() {
+            // only fall back to the discovered DSpark sidecar when no draft was explicitly provided
+            if (params.speculative.draft.mparams.empty()) {
+                params.speculative.draft.mparams.path = hf_cache::finalize_file(plan.dspark);
+            } else {
+                hf_cache::finalize_file(plan.dspark);
+            }
+        });
+    }
     if (!plan.preset.local_path.empty()) {
         tasks.emplace_back(plan.preset, opts, [&]() {
             // if HF repo is a preset repo, we simply run server in router mode with the preset.ini file
@@ -689,12 +715,61 @@ void common_models_handler_apply(common_models_handler & handler, common_params
 // CLI argument parsing functions
 //
 
+// apply config files (if present), a later file overrides an earlier one:
+// 1. system-wide: /etc/llama.cpp/config.ini (%PROGRAMDATA%\llama.cpp\config.ini on windows)
+// 2. user-level: ${XDG_CONFIG_HOME:-~/.config}/llama.cpp/config.ini (%APPDATA%\llama.cpp\config.ini on windows)
+static void common_params_apply_system_config(common_params & params, llama_example ex) {
+    std::vector<std::string> paths;
+
+#if defined(_WIN32)
+    const std::string program_data = common_get_env("PROGRAMDATA");
+    if (!program_data.empty()) {
+        paths.push_back(program_data + "\\llama.cpp\\config.ini");
+    }
+#else
+    paths.push_back("/etc/llama.cpp/config.ini");
+#endif
+
+    try {
+        paths.push_back(fs_get_config_directory() + "config.ini");
+    } catch (const std::exception & e) {
+        LOG_DBG("cannot read user-level config file, skipping: %s\n", e.what());
+    }
+
+    std::vector<std::string> found;
+    for (const auto & path : paths) {
+        std::error_code ec;
+        if (std::filesystem::exists(path, ec)) {
+            found.push_back(path);
+        }
+    }
+    if (found.empty()) {
+        return;
+    }
+
+    common_preset_context ctx(ex);
+    ctx.ignore_unknown_keys = true; // the same config file is shared by all programs
+    for (const auto & path : found) {
+        LOG_INF("using config file: %s\n", path.c_str());
+        common_preset global;
+        common_presets presets = ctx.load_from_ini(path, global);
+        global.apply_to_params(params);
+        auto it = presets.find(COMMON_PRESET_DEFAULT_NAME);
+        if (it != presets.end()) {
+            it->second.apply_to_params(params);
+        }
+    }
+}
+
 static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
     common_params & params = ctx_arg.params;
 
     // setup log directly from params.verbosity: see tools/cli/cli.cpp
     common_log_set_verbosity_thold(params.verbosity);
 
+    // config file applies first, so env variables and CLI arguments override it
+    common_params_apply_system_config(params, ctx_arg.ex);
+
     std::unordered_map<std::string, std::pair<common_arg *, bool>> arg_to_options;
     for (auto & opt : ctx_arg.options) {
         for (const auto & arg : opt.args) {
@@ -1327,6 +1402,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         params.n_parallel = -1;     // auto by default
     } else if (ex == LLAMA_EXAMPLE_TOKENIZE) {
         params.parse_special = true; // parse special tokens by default, like the old tokenize tool
+    } else if (ex == LLAMA_EXAMPLE_TTS) {
+        params.out_file = "output.wav";
+        params.sampling.penalty_repeat = 1.05f;
+        params.sampling.penalty_last_n = -1;
     }
 
     params.use_color = tty_can_use_colors();
@@ -1371,8 +1450,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--version"},
         "show version and build info",
         [](common_params &) {
-            fprintf(stderr, "version: %d (%s)\n", llama_build_number(), llama_commit());
-            fprintf(stderr, "built with %s for %s\n", llama_compiler(), llama_build_target());
+            llama_print_build_info(llama_version());
             exit(0);
         }
     ));
@@ -1989,9 +2067,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sampling());
     add_opt(common_arg(
         {"--repeat-last-n"}, "N",
-        string_format("last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", params.sampling.penalty_last_n),
+        string_format("last n tokens to consider for penalize (default: %d, 0 = disabled)", params.sampling.penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
+            if (value < 0) {
                 throw std::runtime_error(string_format("error: invalid repeat-last-n = %d\n", value));
             }
             params.sampling.penalty_last_n = value;
@@ -2003,7 +2081,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--repeat-penalty"}, "N",
         string_format("penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)", (double)params.sampling.penalty_repeat),
         [](common_params & params, const std::string & value) {
-            params.sampling.penalty_repeat = std::stof(value);
+            const float penalty_repeat = std::stof(value);
+            if (!std::isfinite(penalty_repeat) ||
+                penalty_repeat <= 0.0f ||
+                !std::isfinite(1.0f/penalty_repeat)) {
+                throw std::runtime_error("error: repeat-penalty must be finite and greater than 0\n");
+            }
+            params.sampling.penalty_repeat = penalty_repeat;
             params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT;
         }
     ).set_sampling());
@@ -2011,14 +2095,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--presence-penalty"}, "N",
         string_format("repeat alpha presence penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_present),
         [](common_params & params, const std::string & value) {
-            params.sampling.penalty_present = std::stof(value);
+            const float penalty_present = std::stof(value);
+            if (!std::isfinite(penalty_present)) {
+                throw std::runtime_error("error: presence-penalty must be finite\n");
+            }
+            params.sampling.penalty_present = penalty_present;
         }
     ).set_sampling());
     add_opt(common_arg(
         {"--frequency-penalty"}, "N",
         string_format("repeat alpha frequency penalty (default: %.2f, 0.0 = disabled)", (double)params.sampling.penalty_freq),
         [](common_params & params, const std::string & value) {
-            params.sampling.penalty_freq = std::stof(value);
+            const float penalty_freq = std::stof(value);
+            if (!std::isfinite(penalty_freq)) {
+                throw std::runtime_error("error: frequency-penalty must be finite\n");
+            }
+            params.sampling.penalty_freq = penalty_freq;
         }
     ).set_sampling());
     add_opt(common_arg(
@@ -2048,9 +2140,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_sampling());
     add_opt(common_arg(
         {"--dry-penalty-last-n"}, "N",
-        string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable, -1 = context size)", params.sampling.dry_penalty_last_n),
+        string_format("set DRY penalty for the last n tokens (default: %d, 0 = disable)", params.sampling.dry_penalty_last_n),
         [](common_params & params, int value) {
-            if (value < -1) {
+            if (value < 0) {
                 throw std::runtime_error(string_format("error: invalid dry-penalty-last-n = %d\n", value));
             }
             params.sampling.dry_penalty_last_n = value;
@@ -2534,7 +2626,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.mtmd_batch_max_tokens = value;
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MTMD_BATCH_MAX_TOKENS"));
-    if (llama_supports_rpc()) {
+    if (params.is_gen_docs || llama_supports_rpc()) {
         add_opt(common_arg(
             {"--rpc"}, "SERVERS",
             "comma-separated list of RPC servers (host:port)",
@@ -2572,14 +2664,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_env("LLAMA_ARG_DIO"));
     add_opt(common_arg(
         {"-lm", "--load-mode"}, "MODE",
-        "model loading mode (default: mmap)\n"
+        "model loading mode (default: auto)\n"
+        "- auto: mmap, unless a device does not support it\n"
         "- none: no special loading mode\n"
         "- mmap: memory-map model (if mmap disabled, slower load but may reduce pageouts if not using mlock)\n"
         "- mlock: force system to keep model in RAM rather than swapping or compressing\n"
         "- mmap+mlock: mmap + force system to keep model in RAM rather than swapping or compressing\n"
         "- dio: use DirectIO if available\n",
         [](common_params & params, const std::string & value) {
-            /**/ if (value == "none")       { params.load_mode = LLAMA_LOAD_MODE_NONE;       }
+            /**/ if (value == "auto")       { params.load_mode = LLAMA_LOAD_MODE_AUTO;       }
+            else if (value == "none")       { params.load_mode = LLAMA_LOAD_MODE_NONE;       }
             else if (value == "mmap")       { params.load_mode = LLAMA_LOAD_MODE_MMAP;       }
             else if (value == "mlock")      { params.load_mode = LLAMA_LOAD_MODE_MLOCK;      }
             else if (value == "mmap+mlock") { params.load_mode = LLAMA_LOAD_MODE_MMAP_MLOCK; }
@@ -2935,20 +3029,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             params.model.hf_file = value;
         }
     ).set_examples({LLAMA_EXAMPLE_COMMON, LLAMA_EXAMPLE_DOWNLOAD, LLAMA_EXAMPLE_TOKENIZE}).set_env("LLAMA_ARG_HF_FILE"));
-    add_opt(common_arg(
-        {"-hfv", "-hfrv", "--hf-repo-v"}, "<user>/<model>[:quant]",
-        "Hugging Face model repository for the vocoder model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.vocoder.model.hf_repo = value;
-        }
-    ).set_env("LLAMA_ARG_HF_REPO_V"));
-    add_opt(common_arg(
-        {"-hffv", "--hf-file-v"}, "FILE",
-        "Hugging Face model file for the vocoder model (default: unused)",
-        [](common_params & params, const std::string & value) {
-            params.vocoder.model.hf_file = value;
-        }
-    ).set_env("LLAMA_ARG_HF_FILE_V"));
     add_opt(common_arg(
         {"-hft", "--hf-token"}, "TOKEN",
         "Hugging Face access token (default: value from HF_TOKEN environment variable)",
@@ -3283,12 +3363,23 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--tools"}, "TOOL1,TOOL2,...",
         "experimental: whether to enable built-in tools for AI agents - do not enable in untrusted environments (default: no tools)\n"
         "specify \"all\" to enable all tools\n"
-        "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_datetime\n"
+        "available tools: read_file, file_glob_search, grep_search, exec_shell_command, write_file, edit_file, get_datetime, get_info\n"
         "note: for security reasons, this will limit --cors-origins to localhost by default",
         [](common_params & params, const std::string & value) {
             params.server_tools = parse_csv_row(value);
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS"));
+    add_opt(common_arg(
+        {"--tools-runtime"}, "OPTION",
+        "experimental: run tools in a separate runtime environment (default: none, use host environment)\n"
+        "available options:\n"
+        "  'docker:<image>', 'podman:<image>': spin up a new container and reuse it for all invocations, clean up on server exit\n"
+        "  'docker-container:<id>', 'podman-container:<id>': use an existing container by ID, won't stop on server exit\n"
+        "  'ssh:<target>': run tools on a remote POSIX host over SSH, key-based auth and a trusted host key are required\n",
+        [](common_params & params, const std::string & value) {
+            params.server_tools_runtime = value;
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_TOOLS_RUNTIME"));
     add_opt(common_arg(
         {"--mcp-servers-config"}, "PATH",
         "experimental: path to JSON file with MCP server definitions (Cursor-compatible format) - do not enable in untrusted environments (default: none)\n"
@@ -3556,6 +3647,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
             }
         }
     ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING"));
+    add_opt(common_arg(
+        {"--reasoning-effort"}, "LEVEL",
+        "reasoning effort level given to the chat template: 'default' to keep the template default,\n"
+        "or a level such as 'minimal', 'low', 'medium', 'high', 'xhigh' or 'max' (default: default)",
+        [](common_params & params, const std::string & value) {
+            if (value == "default") {
+                params.default_template_kwargs.erase("reasoning_effort");
+            } else {
+                params.default_template_kwargs["reasoning_effort"] = json(value).dump();
+            }
+        }
+    ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_REASONING_EFFORT"));
     add_opt(common_arg(
         {"--reasoning-budget"}, "N",
         "token budget for thinking: -1 for unrestricted, 0 for immediate end, N>0 for token budget (default: -1)",
@@ -3975,6 +4078,9 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
         {"--spec-draft-n-max"}, "N",
         string_format("number of tokens to draft for speculative decoding (default: %d)", params.speculative.draft.n_max),
         [](common_params & params, int value) {
+            if (value < 0) {
+                throw std::invalid_argument("invalid value");
+            }
             params.speculative.draft.n_max = value;
         }
     ).set_spec().set_examples({LLAMA_EXAMPLE_SPECULATIVE, LLAMA_EXAMPLE_LOOKUP, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SPEC_DRAFT_N_MAX"));
@@ -4224,24 +4330,18 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     //
 
     add_opt(common_arg(
-        {"-mv", "--model-vocoder"}, "FNAME",
-        "vocoder model for audio generation (default: unused)",
+        {"--tts-lang"}, "FNAME",
+        "language (ISO 639-1) for audio generation\n"
+        "see tts/README.md for per-model usage notes",
         [](common_params & params, const std::string & value) {
-            params.vocoder.model.path = value;
+            params.tts_lang = value;
         }
-    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
-     add_opt(common_arg(
-        {"--tts-use-guide-tokens"},
-        "Use guide tokens to improve TTS word recall",
-        [](common_params & params) {
-            params.vocoder.use_guide_tokens = true;
-        }
-    ).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
+    ).set_examples({LLAMA_EXAMPLE_TTS}));
     add_opt(common_arg(
         {"--tts-speaker-file"}, "FNAME",
         "speaker file path for audio generation",
         [](common_params & params, const std::string & value) {
-            params.vocoder.speaker_file = value;
+            params.tts_speaker_file = value;
         }
     ).set_examples({LLAMA_EXAMPLE_TTS}));
 
@@ -4361,16 +4461,6 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
     ).set_examples({LLAMA_EXAMPLE_DEBUG}));
 
     // presets
-    add_opt(common_arg(
-        {"--tts-oute-default"},
-        string_format("use default OuteTTS models (note: can download weights from the internet)"),
-        [](common_params & params) {
-            params.model.hf_repo = "OuteAI/OuteTTS-0.2-500M-GGUF";
-            params.model.hf_file = "OuteTTS-0.2-500M-Q8_0.gguf";
-            params.vocoder.model.hf_repo = "ggml-org/WavTokenizer";
-            params.vocoder.model.hf_file = "WavTokenizer-Large-75-F16.gguf";
-        }
-    ).set_examples({LLAMA_EXAMPLE_TTS}));
 
     add_opt(common_arg(
         {"--embd-gemma-default"},

common/arg.h

@@ -137,7 +137,6 @@ void common_params_add_preset_options(std::vector<common_arg> & args);
 struct common_models_handler {
     common_download_hf_plan plan;
     common_download_hf_plan plan_spec;
-    common_download_hf_plan plan_voc;
     common_download_opts opts;
 };
 

common/build-info.cpp.in

@@ -29,7 +29,7 @@ const char * llama_build_info(void) {
     return s.c_str();
 }
 
-void llama_print_build_info(void) {
-    fprintf(stderr, "%s: build = %d (%s)\n",      __func__, llama_build_number(), llama_commit());
-    fprintf(stderr, "%s: built with %s for %s\n", __func__, llama_compiler(), llama_build_target());
+void llama_print_build_info(const char * llama_version) {
+    fprintf(stderr, "version: %s (build %d, commit %s)\n", llama_version, llama_build_number(), llama_commit());
+    fprintf(stderr, "built with %s for %s\n", llama_compiler(), llama_build_target());
 }

common/build-info.h

@@ -2,6 +2,7 @@
 #define BUILD_INFO_H
 
 #define LLAMA_BUILD_NUMBER 999
+#define LLAMA_VERSION "1.0"
 #define LLAMA_COMMIT "KOBOLDCPP"
 #define LLAMA_COMPILER "KCPP"
 #define LLAMA_TARGET "KCPP"
@@ -32,7 +33,7 @@ static inline const char * llama_build_info(void) {
     return s.c_str();
 }
 
-static inline void llama_print_build_info(void) {
+static inline void llama_print_build_info(const char *) {
     fprintf(stderr, "%s: build = %d (%s)\n",      __func__, llama_build_number(), llama_commit());
     fprintf(stderr, "%s: built with %s for %s\n", __func__, llama_compiler(), llama_build_target());
 }

common/chat-peg-parser.cpp

@@ -6,6 +6,9 @@
 
 #include <nlohmann/json.hpp>
 
+#include <cstdint>
+#include <functional>
+
 using ordered_json = nlohmann::ordered_json;
 
 static std::string_view trim_trailing_space(std::string_view sv, int max = -1) {
@@ -235,6 +238,43 @@ common_peg_parser common_chat_peg_builder::tag_with_safe_content(const std::stri
     return zero_or_more(choice({ p, content_chunk }));
 }
 
+common_peg_parser common_chat_peg_builder::permute(const std::string &                    rule_prefix,
+                                                   const std::vector<common_peg_parser> & parsers) {
+    if (parsers.empty()) {
+        return eps();
+    }
+
+    if (parsers.size() == 1 || parsers.size() > COMMON_CHAT_MAX_PERMUTE) {
+        return sequence(parsers);
+    }
+
+    std::map<uint32_t, common_peg_parser>      rules;
+    std::function<common_peg_parser(uint32_t)> remaining_of;
+
+    remaining_of = [&](uint32_t remaining) -> common_peg_parser {
+        if (remaining == 0) {
+            return eps();
+        }
+
+        auto cached = rules.find(remaining);
+        if (cached != rules.end()) {
+            return cached->second;
+        }
+
+        auto alternatives = choice();
+        for (size_t i = 0; i < parsers.size(); i++) {
+            const uint32_t bit = 1u << i;
+            if (remaining & bit) {
+                alternatives |= parsers[i] + remaining_of(remaining & ~bit);
+            }
+        }
+
+        return rules.emplace(remaining, rule(rule_prefix + "-" + std::to_string(remaining), alternatives)).first->second;
+    };
+
+    return remaining_of((1u << parsers.size()) - 1);
+}
+
 std::string & common_chat_peg_mapper::args_target() {
     return (current_tool && !current_tool->name.empty()) ? current_tool->arguments : args_buffer;
 }
@@ -554,9 +594,7 @@ common_peg_parser common_chat_peg_builder::python_style_tool_calls(
 
                 // Full argument: name="value" or name=value
                 auto arg_rule = tool_arg(
-                    tool_arg_open(eps()) +
-                    tool_arg_name(arg_name_parser) +
-                    literal("=") +
+                    tool_arg_open(tool_arg_name(arg_name_parser) + literal("=")) +
                     arg_value_parser +
                     tool_arg_close(eps())
                 );

common/chat-peg-parser.h

@@ -55,6 +55,8 @@ class common_chat_peg_minimax_m3_mapper : public common_chat_peg_mapper {
 struct content_structure;
 struct tool_call_structure;
 
+constexpr size_t COMMON_CHAT_MAX_PERMUTE = 6;
+
 class common_chat_peg_builder : public common_peg_parser_builder {
   public:
     // Tag constants (from former common_chat_peg_base_builder)
@@ -105,6 +107,9 @@ class common_chat_peg_builder : public common_peg_parser_builder {
     common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return tag(TOOL_ARG_VALUE, p); }
 
 
+    // Matches every parser exactly once, in any order.
+    common_peg_parser permute(const std::string & rule_prefix, const std::vector<common_peg_parser> & parsers);
+
     // Return a parser that parses the prefix of a string, up to a given delimiter.
     common_peg_parser prefix(const std::string & s, const std::string & delimiter = {});
 

common/chat.cpp

@@ -934,6 +934,10 @@ static std::string common_chat_template_direct_apply_impl(
         bool enabled = inp["preserve_reasoning"].get<bool>();
         jinja::caps_apply_preserve_reasoning(ctx, enabled);
     }
+    if (inp.contains("reasoning_effort") && inp["reasoning_effort"].is_string() && !inp["reasoning_effort"].empty()) {
+        std::string reasoning_effort = inp["reasoning_effort"].get<std::string>();
+        jinja::caps_apply_reasoning_effort(ctx, reasoning_effort);
+    }
 
     jinja::global_from_json(ctx, inp, inputs.mark_input);
 
@@ -1124,6 +1128,179 @@ static common_chat_params common_chat_params_init_ministral_3(const common_chat_
     return data;
 }
 
+static common_chat_params common_chat_params_init_qwen3_coder(const common_chat_template &          tmpl,
+                                                              const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    const std::string GEN_PREFIX = "<|im_start|>assistant\n";
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(tmpl, inputs);
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+
+    auto supports_reasoning = tmpl.source().find("<think>") != std::string::npos;
+
+    data.supports_thinking = supports_reasoning;
+    data.preserved_tokens  = {
+        "<tool_call>",
+        "</tool_call>",
+    };
+
+    if (supports_reasoning) {
+        data.thinking_start_tag = "<think>";
+        // Support both </think> and <tool_call> as reasoning end sequences.
+        // <function= is omitted, as it is a workaround for Qwen3-Coder which is not a thinking model
+        data.thinking_end_tags = { "</think>", "<tool_call>" };
+        data.preserved_tokens.insert(data.preserved_tokens.end(), { "<think>", "</think>" });
+    }
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|im_start|>assistant"             },
+        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>user\n<tool_response>" }, // Qwen3-Coder, Qwen3.5, Nemotron Nano 3
+        { COMMON_CHAT_ROLE_TOOL,      "<|im_start|>tool_response"         }, // StepFun-3.5-Flash
+        { COMMON_CHAT_ROLE_USER,      "<|im_start|>user"                  },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|im_start|>system"                },
+    };
+
+    auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
+    auto has_response_format = inputs.json_schema.is_object() && !inputs.json_schema.empty();
+    auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+    auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = GEN_PREFIX;
+        if (supports_reasoning) {
+            data.generation_prompt += "<think>\n" + msg.reasoning_content;
+            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+                data.generation_prompt += "\n</think>\n\n";
+            }
+        }
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    std::vector<std::string> tool_call_starts = { "<tool_call>" };
+
+    // Match complete <function=name> opener for Qwen3-Coder models that occasionally omit the
+    // starting <tool_call>. The model may hallucinate a tool name, but it is preferable over
+    // constraining on <function which may occur in valid content generation, e.g. #include <functional>
+    foreach_function(inputs.tools, [&](const json & tool) {
+        const std::string name = tool.at("function").at("name");
+        tool_call_starts.push_back("<function=" + name + ">");
+    });
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto generation_prompt = p.literal(GEN_PREFIX);
+
+        auto reasoning = p.eps();
+        if (supports_reasoning && extract_reasoning) {
+            reasoning = p.optional("<think>" + p.space() +
+                                   p.reasoning(p.until_one_of({ "</think>", "<tool_call>" })) +
+                                   (p.literal("</think>") | p.peek(p.literal("<tool_call>"))));
+        }
+
+        // Response format parser
+        if (has_response_format) {
+            return generation_prompt + (reasoning << p.content(p.schema(p.json(), "response-format", inputs.json_schema)));
+        }
+
+        // Tool call parser
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto arg_close  = p.tool_arg_close(p.literal("\n</parameter>\n"));
+            auto arg_string = p.rule("xml-arg-string",
+                p.ac(p.tool_arg_string_value(p.until("\n</parameter>\n")) + arg_close, "\n</parameter>\n"));
+
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function   = tool.at("function");
+                std::string  name       = function.at("name");
+                auto         parameters = function.contains("parameters") ? function.at("parameters") : json::object();
+
+                auto schema_info = common_schema_info();
+                schema_info.resolve_refs(parameters);
+
+                std::vector<common_peg_parser> required_args;
+                std::vector<common_peg_parser> optional_args;
+
+                foreach_parameter(function, [&](const std::string & param_name, const json & param_schema, bool is_required) {
+                    auto rule_name = "tool-" + name + "-arg-" + param_name;
+
+                    auto arg_open = p.tool_arg_open("<parameter=" + p.tool_arg_name(p.literal(param_name)) + ">\n");
+
+                    auto arg_value = schema_info.resolves_to_string(param_schema) ?
+                        arg_string :
+                        p.tool_arg_json_value(p.schema(p.json(), rule_name + "-schema", param_schema)) + arg_close;
+
+                    auto arg_rule = p.rule(rule_name, p.tool_arg(arg_open + arg_value));
+
+                    (is_required ? required_args : optional_args).push_back(arg_rule);
+                });
+
+                // Accept required arguments in any order, as Qwen does not always adhere to the
+                // order provided.
+                auto args = p.permute("tool-" + name + "-args", required_args);
+                if (!optional_args.empty()) {
+                    args = args + p.zero_or_more(p.choice(optional_args));
+                }
+
+                auto func = p.tool(p.tool_open("<function=" + p.tool_name(p.literal(name)) + ">\n") +
+                                   p.tool_args(args) +
+                                   p.tool_close(p.literal("</function>\n")));
+
+                tool_choice |= p.rule("tool-" + name, func);
+            });
+
+            auto min_calls = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED ? 1 : 0;
+
+            // Qwen3-Coder models may occasionally omit the <tool_call> token.
+            auto tool_call_body  = tool_choice + "</tool_call>" + p.space();
+            auto tool_call_first = p.rule("tool-call-first", p.optional(p.literal("<tool_call>\n")) + tool_call_body);
+            auto tool_call       = p.rule("tool-call", "<tool_call>\n" + tool_call_body);
+
+            auto calls      = inputs.parallel_tool_calls ? tool_call_first + p.zero_or_more(tool_call) : tool_call_first;
+            auto tool_calls = p.trigger_rule("tool-call-root", p.repeat(calls, min_calls, 1));
+
+            return generation_prompt +
+                   (reasoning << p.content(p.until_one_of(tool_call_starts)) << tool_calls);
+        }
+
+        // Content only parser
+        return generation_prompt + (reasoning << p.content(p.rest()));
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_AUTO;
+
+        data.grammar = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            if (has_response_format) {
+                auto schema = inputs.json_schema;
+                builder.resolve_refs(schema);
+            }
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+
+        if (data.grammar_lazy) {
+            for (const auto & start : tool_call_starts) {
+                data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, start });
+            }
+        }
+    }
+
+    return data;
+}
+
 static common_chat_params common_chat_params_init_gpt_oss(const common_chat_template &    tmpl,
                                                           const autoparser::generation_params & inputs) {
     common_chat_params data;
@@ -1957,23 +2134,16 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
         adjusted_messages = deepseek_v4_sort_tool_results(inputs.messages);
     }
 
-    data.prompt             = common_chat_template_direct_apply_impl(tmpl, inputs, adjusted_messages);
-    data.generation_prompt  = common_chat_template_generation_prompt_impl(tmpl, inputs, adjusted_messages);
-    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
-    data.supports_thinking  = true;
-    data.thinking_start_tag = "<think>";
-    data.thinking_end_tags  = {"</think>"};
-    data.preserved_tokens   = {
-        "｜DSML｜",
-        "<think>",
-        "</think>",
-    };
-
     auto has_tools           = inputs.tools.is_array() && !inputs.tools.empty();
     auto has_response_format = !inputs.json_schema.is_null() && inputs.json_schema.is_object();
     auto extract_reasoning   = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
     auto include_grammar     = has_response_format || (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE);
 
+    std::optional<json> additional_context;
+    if (is_v4 && has_response_format) {
+        additional_context = json{ { "response_format", inputs.json_schema } };
+    }
+
     const std::string DSML         = "｜DSML｜";
     const std::string THINK_START  = "<think>";
     const std::string THINK_END    = "</think>";
@@ -1985,25 +2155,137 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
     const std::string PARAM_START  = "<" + DSML + "parameter";
     const std::string PARAM_END    = "</" + DSML + "parameter>";
     const std::string GEN_PROMPT   = "<｜Assistant｜>";
+    const std::string TC_SEPARATOR = "\n\n";
+
+    data.prompt = common_chat_template_direct_apply_impl(
+        tmpl, inputs, adjusted_messages, std::nullopt, additional_context);
+    data.generation_prompt = common_chat_template_generation_prompt_impl(
+        tmpl, inputs, adjusted_messages, std::nullopt, additional_context);
+    data.format             = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking  = true;
+    data.thinking_start_tag = THINK_START;
+    data.thinking_end_tags  = {THINK_END, FC_START};
+    data.preserved_tokens   = {
+        DSML,
+        THINK_START,
+        THINK_END,
+    };
 
     if (inputs.has_continuation()) {
         const auto & msg = inputs.continue_msg;
 
-        data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
-        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
-            data.generation_prompt += THINK_END + msg.render_content();
+        if (is_v4 && msg.reasoning_content.empty()) {
+            data.generation_prompt = GEN_PROMPT + THINK_END;
+            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+                data.generation_prompt += msg.render_content();
+            }
+        } else {
+            data.generation_prompt = GEN_PROMPT + THINK_START + msg.reasoning_content;
+            if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+                data.generation_prompt += THINK_END + msg.render_content();
+            }
         }
 
         data.prompt += data.generation_prompt;
     }
 
+    bool require_tools   = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+    bool has_tool_calls = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
     auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
         auto generation_prompt = p.literal(GEN_PROMPT);
-        auto end = p.end();
+        auto end               = p.end();
+
+        // build tool call section first since we might need it in reasoning
+        auto tool_choice = p.choice();
+        if (has_tool_calls) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                std::string  name     = function.at("name");
+                auto         params   = function.contains("parameters") ? function.at("parameters") : json::object();
+                const auto & props    = params.contains("properties") ? params.at("properties") : json::object();
+
+                std::set<std::string> required;
+                if (params.contains("required")) {
+                    params.at("required").get_to(required);
+                }
+
+                auto schema_info = common_schema_info();
+                schema_info.resolve_refs(params);
+
+                std::vector<common_peg_parser> required_parsers;
+                std::vector<common_peg_parser> optional_parsers;
+                for (const auto & [param_name, param_schema] : props.items()) {
+                    bool is_required = required.find(param_name) != required.end();
+                    bool is_string   = schema_info.resolves_to_string(param_schema);
+
+                    auto arg = p.tool_arg(
+                        p.tool_arg_open(p.literal(PARAM_START + " name=\"") + p.tool_arg_name(p.literal(param_name)) +
+                                        p.literal("\" string=\"" + std::string(is_string ? "true" : "false") + "\">")) +
+                        (is_string ?
+                             p.tool_arg_string_value(p.until(PARAM_END)) :
+                             p.tool_arg_json_value(p.schema(p.json(), "tool-" + name + "-arg-" + param_name + "-schema",
+                                                            param_schema, false))) +
+                        p.tool_arg_close(p.literal(PARAM_END)));
+
+                    auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
+                    if (is_required) {
+                        required_parsers.push_back(named_arg);
+                    } else {
+                        optional_parsers.push_back(named_arg);
+                    }
+                }
+
+                common_peg_parser args_seq = p.eps();
+                for (size_t i = 0; i < required_parsers.size(); i++) {
+                    if (i > 0) {
+                        args_seq = args_seq + p.space();
+                    }
+                    args_seq = args_seq + required_parsers[i];
+                }
+
+                if (!optional_parsers.empty()) {
+                    common_peg_parser any_opt = p.choice();
+                    for (const auto & opt : optional_parsers) {
+                        any_opt |= opt;
+                    }
+                    args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1);
+                }
+
+                common_peg_parser invoke_body = args_seq;
+                auto              func_parser = p.tool(p.tool_open(p.literal(INVOKE_START + " name=\"") +
+                                                                   p.tool_name(p.literal(name)) + p.literal("\">\n")) +
+                                                       invoke_body + p.space() + p.tool_close(p.literal(INVOKE_END)));
+
+                tool_choice |= p.rule("tool-" + name, func_parser);
+            });
+        }
+
+        common_peg_parser tool_calls = p.eps();
+        if (inputs.parallel_tool_calls) {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice +
+                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
+        } else {
+            tool_calls = p.trigger_rule("tool-call",
+                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
+        }
 
         auto reasoning = p.eps();
+        auto reasoning_with_tc = p.eps();
+        auto obligatory_tool_calls = tool_calls;
+        bool allow_reasoning_with_tc = false;
+
+        if (!require_tools) {
+            tool_calls = p.optional(tool_calls);
+        }
+
         if (extract_reasoning && inputs.enable_thinking) {
             reasoning = p.optional(THINK_START + p.reasoning(p.until(THINK_END)) + THINK_END);
+            reasoning_with_tc = THINK_START +
+                p.reasoning(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START, THINK_END })) +
+                p.space() + obligatory_tool_calls;
+            allow_reasoning_with_tc = true;
         } else if (extract_reasoning) {
             // Thinking disabled but reasoning extraction requested: the generation prompt
             // contains an empty <think></think> pair (V3.2) or a bare </think> (V4) that
@@ -2021,101 +2303,21 @@ static common_chat_params common_chat_params_init_deepseek_v3_2(const common_cha
             return generation_prompt + reasoning + response_format + end;
         }
 
-        if (!has_tools || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
+        if (!has_tool_calls) {
             return generation_prompt + reasoning + p.content(p.rest()) + end;
         }
 
-        auto tool_choice = p.choice();
-        foreach_function(inputs.tools, [&](const json & tool) {
-            const auto & function = tool.at("function");
-            std::string  name     = function.at("name");
-            auto params   = function.contains("parameters") ? function.at("parameters") : json::object();
-            const auto & props    = params.contains("properties") ? params.at("properties") : json::object();
-
-            std::set<std::string> required;
-            if (params.contains("required")) {
-                params.at("required").get_to(required);
-            }
-
-            auto schema_info = common_schema_info();
-            schema_info.resolve_refs(params);
-
-            std::vector<common_peg_parser> required_parsers;
-            std::vector<common_peg_parser> optional_parsers;
-            for (const auto & [param_name, param_schema] : props.items()) {
-                bool is_required = required.find(param_name) != required.end();
-                bool is_string   = schema_info.resolves_to_string(param_schema);
-
-                auto arg = p.tool_arg(
-                    p.tool_arg_open(
-                        p.literal(PARAM_START + " name=\"") +
-                        p.tool_arg_name(p.literal(param_name)) +
-                        p.literal("\" string=\"" + std::string(is_string ? "true" : "false") + "\">")) +
-                    (is_string
-                         ? p.tool_arg_string_value(p.until(PARAM_END))
-                         : p.tool_arg_json_value(p.schema(p.json(),
-                                                          "tool-" + name + "-arg-" + param_name + "-schema",
-                                                          param_schema, false))) +
-                    p.tool_arg_close(p.literal(PARAM_END)));
-
-                auto named_arg = p.rule("tool-" + name + "-arg-" + param_name, arg);
-                if (is_required) {
-                    required_parsers.push_back(named_arg);
-                } else {
-                    optional_parsers.push_back(named_arg);
-                }
-            }
-
-            common_peg_parser args_seq = p.eps();
-            for (size_t i = 0; i < required_parsers.size(); i++) {
-                if (i > 0) {
-                    args_seq = args_seq + p.space();
-                }
-                args_seq = args_seq + required_parsers[i];
-            }
-
-            if (!optional_parsers.empty()) {
-                common_peg_parser any_opt = p.choice();
-                for (const auto & opt : optional_parsers) {
-                    any_opt |= opt;
-                }
-                args_seq = args_seq + p.repeat(p.space() + any_opt, 0, -1);
-            }
-
-            common_peg_parser invoke_body = args_seq;
-            auto func_parser = p.tool(
-                p.tool_open(p.literal(INVOKE_START + " name=\"") +
-                            p.tool_name(p.literal(name)) + p.literal("\">\n")) +
-                invoke_body + p.space() +
-                p.tool_close(p.literal(INVOKE_END)));
-
-            tool_choice |= p.rule("tool-" + name, func_parser);
-        });
-
-        auto require_tools = inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED;
-
-        common_peg_parser tool_calls = p.eps();
-        if (inputs.parallel_tool_calls) {
-            tool_calls = p.trigger_rule("tool-call",
-                p.literal(FC_START) + p.space() + tool_choice +
-                p.zero_or_more(p.space() + tool_choice) + p.space() + p.literal(FC_END));
-        } else {
-            tool_calls = p.trigger_rule("tool-call",
-                p.literal(FC_START) + p.space() + tool_choice + p.space() + p.literal(FC_END));
-        }
-
-        if (!require_tools) {
-            tool_calls = p.optional(tool_calls);
-        }
-
-        auto content_before_tools = p.content(p.until(FC_START));
-        return generation_prompt + reasoning + content_before_tools + tool_calls + end;
+        auto content_before_tools = p.negate(p.literal(THINK_START)) +
+            p.content(p.until_one_of({ TC_SEPARATOR + FC_START, FC_START })) +
+            p.space();
+        return allow_reasoning_with_tc ? generation_prompt + (reasoning_with_tc | (reasoning + content_before_tools + tool_calls)) + end :
+            generation_prompt + reasoning + content_before_tools + tool_calls + end;
     });
 
     data.parser = parser.save();
 
     if (include_grammar) {
-        data.grammar_lazy = !(has_response_format || (has_tools && inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED));
+        data.grammar_lazy = has_tools && !require_tools;
         data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
             foreach_function(inputs.tools, [&](const json & tool) {
                 const auto & function = tool.at("function");
@@ -2909,6 +3111,153 @@ static common_chat_params common_chat_params_init_minicpm5(const common_chat_tem
     return data;
 }
 
+// An assistant turn is rendered as one or more messages, each
+// "<|start|>assistant to=<recipient><|message|>{content}{END}" where END is
+// <|eom|> (more messages follow) or <|eot|> (end of turn):
+//   - chain-of-thought: to=self, terminated by <|eom|>
+//   - final answer:     to=user, terminated by <|eot|>
+// The generation prompt is just "<|start|>assistant"; the model emits its own
+// " to=...<|message|>".
+static common_chat_params common_chat_params_init_muse_glimmer(const common_chat_template &          tmpl,
+                                                               const autoparser::generation_params & inputs) {
+    common_chat_params data;
+
+    data.prompt            = common_chat_template_direct_apply_impl(tmpl, inputs);
+    data.generation_prompt = "<|start|>assistant";
+    data.format            = COMMON_CHAT_FORMAT_PEG_NATIVE;
+    data.supports_thinking = true;
+
+    data.preserved_tokens = {
+        "<|start|>", "<|message|>", "<|eom|>", "<|eot|>",
+        // ATEM tool-call markup emitted on " to=<tool>" turns.
+        "<atem:function_calls>", "<atem:invoke", "<atem:parameter", "</atem:parameter>",
+        "</atem:invoke>", "</atem:function_calls>",
+    };
+
+    data.message_delimiters = {
+        { COMMON_CHAT_ROLE_ASSISTANT, "<|start|>assistant" },
+        { COMMON_CHAT_ROLE_USER,      "<|start|>user"      },
+        { COMMON_CHAT_ROLE_SYSTEM,    "<|start|>system"    },
+        { COMMON_CHAT_ROLE_TOOL,      "<|start|>tool"      },
+    };
+
+    if (inputs.has_continuation()) {
+        const auto & msg = inputs.continue_msg;
+
+        data.generation_prompt = "<|start|>assistant to=self<|message|>" + msg.reasoning_content;
+        if (inputs.continue_final_message == COMMON_CHAT_CONTINUATION_CONTENT) {
+            data.generation_prompt += "<|eom|><|start|>assistant to=user<|message|>" + msg.render_content();
+        }
+
+        data.prompt += data.generation_prompt;
+    }
+
+    auto extract_reasoning = inputs.reasoning_format != COMMON_REASONING_FORMAT_NONE;
+
+    auto has_tools = inputs.tools.is_array() && !inputs.tools.empty();
+    // Constrained grammar whenever tools are offered.
+    auto include_grammar = has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE;
+
+    auto parser = build_chat_peg_parser([&](common_chat_peg_builder & p) {
+        auto start = p.rule("start", p.literal("<|start|>assistant"));
+
+        if (!extract_reasoning && !include_grammar) {
+            return start + p.content(p.rest());
+        }
+
+        if (extract_reasoning) {
+            p.rule("analysis", p.literal(" to=self<|message|>") + p.reasoning(p.until("<|eom|>")) + p.literal("<|eom|>"));
+        } else {
+            p.rule("analysis", p.literal(" to=self<|message|>") + p.content(p.until("<|eom|>")) + p.literal("<|eom|>"));
+        }
+        auto analysis = p.ref("analysis");
+
+        auto recipient  = p.optional(p.literal(" to=user"));
+        auto final_msg  = p.rule("final", recipient + p.literal("<|message|>") +
+                                              p.content(p.until_one_of({ "<|eot|>", "<|eom|>" })));
+
+        if (has_tools && inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_NONE) {
+            auto string_value = p.ac(
+                p.tool_arg_string_value(p.until("</atem:parameter>")) + p.tool_arg_close(p.literal("</atem:parameter>")),
+                "</atem:parameter>");
+
+            auto tool_choice = p.choice();
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto &      function = tool.at("function");
+                const std::string name     = function.at("name");
+                auto              params   = function.contains("parameters") ? function.at("parameters") : json::object();
+
+                auto args = p.eps();
+                if (params.contains("properties") && params.at("properties").is_object() && !params.at("properties").empty()) {
+                    auto schema_info = common_schema_info();
+                    schema_info.resolve_refs(params);
+
+                    auto arg_choice = p.choice();
+                    for (const auto & [prop_name, prop_schema] : params.at("properties").items()) {
+                        auto value_parser = p.eps();
+                        if (schema_info.resolves_to_string(prop_schema)) {
+                            value_parser = string_value;
+                        } else {
+                            value_parser = p.tool_arg_json_value(
+                                    p.schema(p.json(), "tool-" + name + "-arg-" + prop_name + "-schema", prop_schema, false))
+                                + p.tool_arg_close(p.literal("</atem:parameter>"));
+                        }
+
+                        auto arg_rule = p.tool_arg(
+                            p.tool_arg_open(p.literal("<atem:parameter name=\"") + p.tool_arg_name(p.literal(prop_name)) + p.literal("\">")) +
+                            value_parser);
+
+                        arg_choice |= arg_rule;
+                    }
+                    args = p.zero_or_more(arg_choice + p.space());
+                }
+
+                auto tool_parser = p.tool(
+                    p.tool_open(p.literal(" to=") + p.until("<|message|>") +
+                                p.literal("<|message|><atem:function_calls>") + p.space() +
+                                p.literal("<atem:invoke name=\"") + p.tool_name(p.literal(name)) + p.literal("\">") + p.space())
+                    << p.tool_args(args)
+                    << p.tool_close(p.literal("</atem:invoke>") + p.space() + p.literal("</atem:function_calls>")));
+
+                tool_choice |= p.rule("tool-" + name, tool_parser);
+            });
+
+            auto tool_calls = inputs.parallel_tool_calls
+                ? p.trigger_rule("tool-call", tool_choice + p.zero_or_more(p.literal("<|eom|>") + start + tool_choice))
+                : p.trigger_rule("tool-call", tool_choice);
+
+
+            if (inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_REQUIRED) {
+                return p.zero_or_more(start + analysis) + start + tool_calls;
+            }
+            auto trailing_calls = p.optional(p.literal("<|eom|>") + start + tool_calls);
+            return p.zero_or_more(start + analysis) + start + (tool_calls | (final_msg + trailing_calls));
+        }
+
+        return p.zero_or_more(start + analysis) + start + final_msg;
+    });
+
+    data.parser = parser.save();
+
+    if (include_grammar) {
+        data.grammar_lazy = inputs.tool_choice != COMMON_CHAT_TOOL_CHOICE_REQUIRED;
+        data.grammar      = build_grammar([&](const common_grammar_builder & builder) {
+            foreach_function(inputs.tools, [&](const json & tool) {
+                const auto & function = tool.at("function");
+                auto         schema   = function.contains("parameters") ? function.at("parameters") : json::object();
+                builder.resolve_refs(schema);
+            });
+            parser.build_grammar(builder, data.grammar_lazy);
+        });
+        data.grammar_triggers = {
+            { COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
+              "<\\|start\\|>assistant( to=(?!self<\\|message\\|>)(?!user<\\|message\\|>)[^<]*?<\\|message\\|>)" },
+        };
+    }
+
+    return data;
+}
+
 static json common_chat_extra_context() {
     json ctx = json::object();
     std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
@@ -2937,6 +3286,12 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_gpt_oss(tmpl, params);
     }
 
+    // Muse Glimmer format using " to=<recipient>" recipients and <|eom|>/<|eot|> message terminators.
+    if (src.find("<atem:function_calls>") != std::string::npos && src.find("<|eom|>") != std::string::npos) {
+        LOG_DBG("Using specialized template: Muse Glimmer\n");
+        return common_chat_params_init_muse_glimmer(tmpl, params);
+    }
+
     // Functionary v3.2 - uses recipient-based format with >>>recipient\n{content}
     // Detection: template has ">>>all" for content and ">>>" prefix for tool calls
     if (src.find(">>>all") != std::string::npos && src.find(">>>${recipient}") != std::string::npos) {
@@ -3020,6 +3375,14 @@ std::optional<common_chat_params> common_chat_try_specialized_template(
         return common_chat_params_init_minicpm5(tmpl, params);
     }
 
+    // Qwen3-Coder XML tool calls, also used by Nemotron Nano 3, Qwen3.5 and StepFun-3.5-Flash
+    if (src.find("<tool_call>") != std::string::npos &&
+        src.find("<function=") != std::string::npos &&
+        src.find("<parameter=") != std::string::npos) {
+        LOG_DBG("Using specialized template: Qwen3-Coder\n");
+        return common_chat_params_init_qwen3_coder(tmpl, params);
+    }
+
     return std::nullopt;
 }
 

common/common.cpp

@@ -1004,24 +1004,42 @@ bool fs_is_directory(const std::string & path) {
     return std::filesystem::exists(dir) && std::filesystem::is_directory(dir);
 }
 
+std::string common_get_env(const std::string & name) {
+    const char * value = std::getenv(name.c_str());
+    return value == nullptr ? "" : value;
+}
+
+void common_set_env(const std::string & name, const std::string & value) {
+#if defined(_WIN32)
+    _putenv_s(name.c_str(), value.c_str());
+#else
+    if (value.empty()) {
+        unsetenv(name.c_str());
+    } else {
+        setenv(name.c_str(), value.c_str(), 1);
+    }
+#endif
+}
+
 std::string fs_get_cache_directory() {
     std::string cache_directory = "";
     auto ensure_trailing_slash = [](std::string p) {
         // Make sure to add trailing slash
-        if (p.back() != DIRECTORY_SEPARATOR) {
+        if (p.empty() || p.back() != DIRECTORY_SEPARATOR) {
             p += DIRECTORY_SEPARATOR;
         }
         return p;
     };
-    if (getenv("LLAMA_CACHE")) {
-        cache_directory = std::getenv("LLAMA_CACHE");
-    } else {
+    cache_directory = common_get_env("LLAMA_CACHE");
+    if (cache_directory.empty()) {
 #if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || \
         defined(__OpenBSD__) || defined(__NetBSD__)
-        if (std::getenv("XDG_CACHE_HOME")) {
-            cache_directory = std::getenv("XDG_CACHE_HOME");
-        } else if (std::getenv("HOME")) {
-            cache_directory = std::getenv("HOME") + std::string("/.cache/");
+        const std::string xdg_cache_home = common_get_env("XDG_CACHE_HOME");
+        const std::string home           = common_get_env("HOME");
+        if (!xdg_cache_home.empty()) {
+            cache_directory = xdg_cache_home;
+        } else if (!home.empty()) {
+            cache_directory = home + "/.cache/";
         } else {
 #if defined(__linux__)
             /* no $HOME is defined, fallback to getpwuid */
@@ -1036,9 +1054,16 @@ std::string fs_get_cache_directory() {
 #endif /* defined(__linux__) */
         }
 #elif defined(__APPLE__)
-        cache_directory = std::getenv("HOME") + std::string("/Library/Caches/");
+        cache_directory = common_get_env("HOME");
+        if (cache_directory.empty()) {
+            throw std::runtime_error("Failed to find $HOME directory");
+        }
+        cache_directory += "/Library/Caches/";
 #elif defined(_WIN32)
-        cache_directory = std::getenv("LOCALAPPDATA");
+        cache_directory = common_get_env("LOCALAPPDATA");
+        if (cache_directory.empty()) {
+            throw std::runtime_error("Failed to find %LOCALAPPDATA% directory");
+        }
 #elif defined(__EMSCRIPTEN__)
         GGML_ABORT("not implemented on this platform");
 #else
@@ -1050,6 +1075,51 @@ std::string fs_get_cache_directory() {
     return ensure_trailing_slash(cache_directory);
 }
 
+std::string fs_get_config_directory() {
+    std::string config_directory = "";
+    auto ensure_trailing_slash = [](std::string p) {
+        if (p.empty() || p.back() != DIRECTORY_SEPARATOR) {
+            p += DIRECTORY_SEPARATOR;
+        }
+        return p;
+    };
+#if defined(__linux__) || defined(__FreeBSD__) || defined(_AIX) || \
+        defined(__OpenBSD__) || defined(__NetBSD__) || defined(__APPLE__)
+    const std::string xdg_config_home = common_get_env("XDG_CONFIG_HOME");
+    const std::string home            = common_get_env("HOME");
+    if (!xdg_config_home.empty()) {
+        config_directory = xdg_config_home;
+    } else if (!home.empty()) {
+        config_directory = home + "/.config/";
+    } else {
+#if defined(__linux__)
+        /* no $HOME is defined, fallback to getpwuid */
+        struct passwd *pw = getpwuid(getuid());
+        if ((!pw) || (!pw->pw_dir)) {
+            throw std::runtime_error("Failed to find $HOME directory");
+        }
+
+        config_directory = std::string(pw->pw_dir) + std::string("/.config/");
+#else
+        throw std::runtime_error("Failed to find $HOME directory");
+#endif
+    }
+#elif defined(_WIN32)
+    config_directory = common_get_env("APPDATA");
+    if (config_directory.empty()) {
+        throw std::runtime_error("Failed to find %APPDATA% directory");
+    }
+#elif defined(__EMSCRIPTEN__)
+    // caller decides what to do when there is no config directory
+    throw std::runtime_error("not implemented on this platform");
+#else
+#  error Unknown architecture
+#endif
+    config_directory = ensure_trailing_slash(config_directory);
+    config_directory += "llama.cpp";
+    return ensure_trailing_slash(config_directory);
+}
+
 std::string fs_get_cache_file(const std::string & filename) {
     GGML_ASSERT(filename.find(DIRECTORY_SEPARATOR) == std::string::npos);
     std::string cache_directory = fs_get_cache_directory();
@@ -1211,6 +1281,8 @@ struct common_init_result::impl {
 
     // note: the order in which model, context, etc. are declared matters because their destructors will be called bottom-to-top
 
+    common_threadpools threadpools;
+
     llama_model_ptr   model;
     llama_context_ptr context;
 
@@ -1291,16 +1363,6 @@ common_init_result::common_init_result(common_params & params, bool model_only)
                 params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end());
     }
 
-    //if (params.sampling.penalty_last_n == -1) {
-    //    LOG_TRC("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-    //    params.sampling.penalty_last_n = llama_n_ctx(lctx);
-    //}
-
-    //if (params.sampling.dry_penalty_last_n == -1) {
-    //    LOG_TRC("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx));
-    //    params.sampling.dry_penalty_last_n = llama_n_ctx(lctx);
-    //}
-
     // init the backend samplers as part of the context creation
     pimpl->samplers.resize(cparams.n_seq_max);
     pimpl->samplers_seq_config.resize(cparams.n_seq_max);
@@ -1322,6 +1384,10 @@ common_init_result::common_init_result(common_params & params, bool model_only)
     }
 
     pimpl->context.reset(lctx);
+
+    set_process_priority(params.cpuparams.priority);
+
+    pimpl->threadpools.init(lctx, params);
 }
 
 llama_model * common_init_result::model() {
@@ -1468,18 +1534,18 @@ common_init_result_ptr common_init_from_params(common_params & params, bool mode
 common_init_result::~common_init_result() = default;
 
 std::string common_get_model_endpoint() {
-    const char * model_endpoint_env = getenv("MODEL_ENDPOINT");
-    // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility.
-    const char * hf_endpoint_env = getenv("HF_ENDPOINT");
-    const char * endpoint_env = model_endpoint_env ? model_endpoint_env : hf_endpoint_env;
-    std::string model_endpoint = "https://huggingface.co/";
-    if (endpoint_env) {
-        model_endpoint = endpoint_env;
-        if (model_endpoint.back() != '/') {
-            model_endpoint += '/';
-        }
+    std::string endpoint = common_get_env("MODEL_ENDPOINT");
+    if (endpoint.empty()) {
+        // the HF_ENDPOINT variable is respected for backward compatibility
+        endpoint = common_get_env("HF_ENDPOINT");
     }
-    return model_endpoint;
+    if (endpoint.empty()) {
+        return "https://huggingface.co/";
+    }
+    if (endpoint.back() != '/') {
+        endpoint += '/';
+    }
+    return endpoint;
 }
 
 char * common_get_model_or_exit(int argc, char * argv[]) {
@@ -1626,6 +1692,7 @@ struct llama_model_params common_model_params_to_llama(common_params & params) {
     mparams.progress_callback           = params.load_progress_callback;
     mparams.progress_callback_user_data = params.load_progress_callback_user_data;
     mparams.no_alloc                    = params.no_alloc;
+    mparams.load_mtp                    = std::find(params.speculative.types.begin(), params.speculative.types.end(), COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
 
     return mparams;
 }
@@ -1637,6 +1704,7 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     cparams.n_seq_max         = params.n_parallel;
     cparams.n_rs_seq          = params.speculative.need_n_rs_seq();
     cparams.n_outputs_max     = std::max(params.n_outputs_max, 0);
+    cparams.n_outputs_max_per_seq = std::max(params.n_outputs_max_per_seq, 0);
     cparams.n_batch           = params.n_batch;
     cparams.n_ubatch          = params.n_ubatch;
     cparams.n_threads         = params.cpuparams.n_threads;
@@ -1668,6 +1736,10 @@ struct llama_context_params common_context_params_to_llama(const common_params &
     return cparams;
 }
 
+//
+// Threadpool utils
+//
+
 struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params) {
     struct ggml_threadpool_params tpp;
 
@@ -1684,6 +1756,56 @@ struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const commo
     return tpp;
 }
 
+common_threadpools::~common_threadpools() {
+    if (!free_fn) {
+        return;
+    }
+    free_fn(threadpool);
+    free_fn(threadpool_batch);
+}
+
+void common_threadpools::init(llama_context * ctx, const common_params & params) {
+    GGML_ASSERT(!threadpool);
+    GGML_ASSERT(!threadpool_batch);
+
+    COM_INF("llama threadpool init, n_threads = %d\n", (int) params.cpuparams.n_threads);
+
+    auto * cpu_dev = ggml_backend_dev_by_type(GGML_BACKEND_DEVICE_TYPE_CPU);
+    if (!cpu_dev) {
+        COM_WRN("%s", "no CPU backend found\n");
+        return;
+    }
+    auto * reg = ggml_backend_dev_backend_reg(cpu_dev);
+    auto * ggml_threadpool_new_fn = (decltype(ggml_threadpool_new) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_new");
+    free_fn = (decltype(ggml_threadpool_free) *) ggml_backend_reg_get_proc_address(reg, "ggml_threadpool_free");
+
+    struct ggml_threadpool_params tpp_batch =
+            ggml_threadpool_params_from_cpu_params(params.cpuparams_batch);
+    struct ggml_threadpool_params tpp =
+            ggml_threadpool_params_from_cpu_params(params.cpuparams);
+
+    if (!ggml_threadpool_params_match(&tpp, &tpp_batch)) {
+        threadpool_batch = ggml_threadpool_new_fn(&tpp_batch);
+        if (!threadpool_batch) {
+            COM_WRN("batch threadpool create failed : n_threads %d\n", tpp_batch.n_threads);
+            return;
+        }
+
+        // start the non-batch threadpool in the paused state
+        tpp.paused = true;
+    }
+
+    threadpool = ggml_threadpool_new_fn(&tpp);
+    if (!threadpool) {
+        COM_WRN("threadpool create failed : n_threads %d\n", tpp.n_threads);
+        free_fn(threadpool_batch);
+        threadpool_batch = nullptr;
+        return;
+    }
+
+    llama_attach_threadpool(ctx, threadpool, threadpool_batch);
+}
+
 //
 // Batch utils
 //

common/common.h

@@ -236,14 +236,14 @@ struct common_params_sampling {
     float   temp               = 0.80f;  // <= 0.0 to sample greedily, 0.0 to not output probabilities
     float   dynatemp_range     = 0.00f;  // 0.0 = disabled
     float   dynatemp_exponent  = 1.00f;  // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t penalty_last_n     = 64;     // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    int32_t penalty_last_n     = 64;     // last n tokens to penalize (0 = disable penalty)
     float   penalty_repeat     = 1.00f;  // 1.0 = disabled
     float   penalty_freq       = 0.00f;  // 0.0 = disabled
     float   penalty_present    = 0.00f;  // 0.0 = disabled
     float   dry_multiplier     = 0.0f;   // 0.0 = disabled;      DRY repetition penalty for tokens extending repetition:
     float   dry_base           = 1.75f;  // 0.0 = disabled;      multiplier * base ^ (length of sequence before token - allowed length)
     int32_t dry_allowed_length = 2;      // tokens extending repetitions beyond this receive penalty
-    int32_t dry_penalty_last_n = -1;     // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
+    int32_t dry_penalty_last_n = 64;     // how many tokens to scan for repetitions (0 = disable penalty)
     float   adaptive_target    = -1.0f;  // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
     float   adaptive_decay     = 0.90f;  // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
     int32_t mirostat           = 0;      // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
@@ -393,14 +393,6 @@ struct common_params_speculative {
     }
 };
 
-struct common_params_vocoder {
-    struct common_params_model model;
-
-    std::string speaker_file; // speaker file path
-
-    bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy
-};
-
 struct common_params_diffusion {
     int32_t steps         = 128;
     bool    visual_mode   = false;
@@ -456,6 +448,7 @@ struct common_params {
     int32_t n_parallel            =     1; // number of parallel sequences to decode
     int32_t n_sequences           =     1; // number of sequences to decode
     int32_t n_outputs_max         =     0; // max outputs in a batch (0 = n_batch)
+    int32_t n_outputs_max_per_seq =     1; // max outputs per sequence
     int32_t grp_attn_n            =     1; // group-attention factor
     int32_t grp_attn_w            =   512; // group-attention width
     int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
@@ -481,7 +474,7 @@ struct common_params {
     std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);
 
     enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
-    enum llama_load_mode  load_mode  = LLAMA_LOAD_MODE_MMAP; // how to load the model
+    enum llama_load_mode  load_mode  = LLAMA_LOAD_MODE_AUTO; // how to load the model
 
     common_cpu_params cpuparams;
     common_cpu_params cpuparams_batch;
@@ -498,7 +491,6 @@ struct common_params {
 
     struct common_params_sampling    sampling;
     struct common_params_speculative speculative;
-    struct common_params_vocoder     vocoder;
     struct common_params_diffusion   diffusion;
 
     struct common_params_model model;
@@ -665,6 +657,7 @@ struct common_params {
 
     // enable built-in tools
     std::vector<std::string> server_tools;
+    std::string server_tools_runtime;
 
     // MCP server configs (Cursor-compatible JSON)
     std::string mcp_servers_config;   // path to JSON file with MCP server definitions
@@ -740,6 +733,12 @@ struct common_params {
     llama_progress_callback load_progress_callback = NULL;
     void *                  load_progress_callback_user_data = NULL;
     bool no_alloc = false; // Don't allocate model buffers
+
+    // TTS params
+    std::string tts_lang = "";
+    std::string tts_speaker_file = "";
+
+    bool is_gen_docs = false; // whether we are running inside llama-gen-docs
 };
 
 // call once at the start of a program if it uses libcommon
@@ -864,6 +863,15 @@ std::string string_from(const struct llama_context * ctx, const struct llama_bat
 
 bool glob_match(const std::string & pattern, const std::string & str);
 
+//
+// Environment utils
+//
+
+// portable environment access, an unset variable reads as an empty string
+// and setting an empty value unsets the variable
+std::string common_get_env(const std::string & name);
+void        common_set_env(const std::string & name, const std::string & value);
+
 //
 // Filesystem utils
 //
@@ -874,6 +882,7 @@ bool fs_is_directory(const std::string & path);
 
 std::string fs_get_cache_directory();
 std::string fs_get_cache_file(const std::string & filename);
+std::string fs_get_config_directory();
 
 struct common_file_info {
     std::string path;
@@ -921,9 +930,8 @@ using common_init_result_ptr = std::unique_ptr<common_init_result>;
 
 common_init_result_ptr common_init_from_params(common_params & params, bool model_only = false);
 
-struct llama_model_params     common_model_params_to_llama  (      common_params & params);
-struct llama_context_params   common_context_params_to_llama(const common_params & params);
-struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params);
+struct llama_model_params   common_model_params_to_llama  (      common_params & params);
+struct llama_context_params common_context_params_to_llama(const common_params & params);
 
 // clear LoRA adapters from context, then apply new list of adapters
 void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
@@ -934,6 +942,28 @@ std::string common_get_model_endpoint();
 // for testing purposes
 char * common_get_model_or_exit(int, char*[]);
 
+//
+// Threadpool utils
+//
+
+struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const common_cpu_params & params);
+
+struct common_threadpools {
+    common_threadpools() = default;
+    ~common_threadpools();
+
+    common_threadpools(const common_threadpools &) = delete;
+    common_threadpools & operator=(const common_threadpools &) = delete;
+
+    void init(llama_context * ctx, const common_params & params);
+
+private:
+    ggml_threadpool * threadpool       = nullptr;
+    ggml_threadpool * threadpool_batch = nullptr;
+
+    decltype(ggml_threadpool_free) * free_fn = nullptr;
+};
+
 //
 // Context utils
 //

common/download.cpp

@@ -656,6 +656,12 @@ static hf_cache::hf_file find_best_dflash(const hf_cache::hf_files & files,
     return find_best_sibling(files, model, "dflash-", tag);
 }
 
+static hf_cache::hf_file find_best_dspark(const hf_cache::hf_files & files,
+                                          const std::string        & model,
+                                          const std::string        & tag = "") {
+    return find_best_sibling(files, model, "dspark-", tag);
+}
+
 static bool gguf_filename_is_model(const std::string & filepath) {
     if (!string_ends_with(filepath, ".gguf")) {
         return false;
@@ -670,7 +676,8 @@ static bool gguf_filename_is_model(const std::string & filepath) {
            filename.find("imatrix") == std::string::npos &&
            filename.find("mtp-")    == std::string::npos &&
            filename.find("eagle3-") == std::string::npos &&
-           filename.find("dflash-") == std::string::npos;
+           filename.find("dflash-") == std::string::npos &&
+           filename.find("dspark-") == std::string::npos;
 }
 
 static hf_cache::hf_file find_best_model(const hf_cache::hf_files & files,
@@ -763,7 +770,7 @@ common_download_hf_plan common_download_get_hf_plan(const common_params_model &
     } else {
         primary = find_best_model(all, tag);
         // a requested sidecar can resolve on its own, without a full model of the same tag
-        if (primary.path.empty() && !opts.download_mtp && !opts.download_dflash && !opts.download_eagle3) {
+        if (primary.path.empty() && !opts.download_mtp && !opts.download_dflash && !opts.download_eagle3 && !opts.download_dspark) {
             LOG_ERR("%s: no GGUF files found in repository %s\n", __func__, repo.c_str());
             list_available_gguf_files(all);
             return plan;
@@ -787,9 +794,12 @@ common_download_hf_plan common_download_get_hf_plan(const common_params_model &
     if (opts.download_eagle3) {
         plan.eagle3 = find_best_eagle3(all, primary.path, tag);
     }
+    if (opts.download_dspark) {
+        plan.dspark = find_best_dspark(all, primary.path, tag);
+    }
 
     if (primary.path.empty() &&
-        plan.mtp.local_path.empty() && plan.dflash.local_path.empty() && plan.eagle3.local_path.empty()) {
+        plan.mtp.local_path.empty() && plan.dflash.local_path.empty() && plan.eagle3.local_path.empty() && plan.dspark.local_path.empty()) {
         LOG_ERR("%s: no GGUF files found in repository %s\n", __func__, repo.c_str());
         list_available_gguf_files(all);
     }
@@ -967,7 +977,8 @@ std::vector<common_cached_model_info> common_list_cached_models() {
             split.prefix.find("mmproj")  != std::string::npos ||
             split.prefix.find("mtp-")    != std::string::npos ||
             split.prefix.find("eagle3-") != std::string::npos ||
-            split.prefix.find("dflash-") != std::string::npos) {
+            split.prefix.find("dflash-") != std::string::npos ||
+            split.prefix.find("dspark-") != std::string::npos) {
             continue;
         }
         if (seen.insert(f.repo_id + ":" + split.tag).second) {

common/download.h

@@ -60,6 +60,7 @@ struct common_download_opts {
     bool download_mtp     = false;
     bool download_eagle3  = false;
     bool download_dflash  = false;
+    bool download_dspark  = false;
     common_download_callback * callback = nullptr;
 };
 
@@ -111,6 +112,7 @@ struct common_download_hf_plan {
     hf_cache::hf_file mtp;
     hf_cache::hf_file eagle3;
     hf_cache::hf_file dflash;
+    hf_cache::hf_file dspark;
     hf_cache::hf_file preset; // if set, only this file is downloaded
 };
 common_download_hf_plan common_download_get_hf_plan(const common_params_model & model, const common_download_opts & opts);

common/fit.cpp

@@ -136,7 +136,10 @@ static std::vector<llama_device_memory_data> common_get_device_memory_data_impl(
         devs.push_back(llama_model_get_device(model, i));
     }
 
-    hp_ngl         = llama_model_n_layer(model) + llama_model_n_layer_nextn(model);
+    hp_ngl         = llama_model_n_layer(model);
+    if (mparams->load_mtp) {
+        hp_ngl    += llama_model_n_layer_nextn(model);
+    }
     hp_n_ctx_train = llama_model_n_ctx_train(model);
     hp_n_expert    = llama_model_n_expert(model);
 

common/imatrix-loader.cpp

@@ -102,7 +102,8 @@ bool common_imatrix_load(const std::string & fname, common_imatrix & imatrix) {
     const int64_t chunk_count_key = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT);
     const int64_t chunk_size_key  = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE);
 
-    if (datasets_key != -1 && gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
+    if (datasets_key != -1 && gguf_get_kv_type(ctx_gguf, datasets_key) == GGUF_TYPE_ARRAY &&
+        gguf_get_arr_type(ctx_gguf, datasets_key) == GGUF_TYPE_STRING) {
         const int64_t n = gguf_get_arr_n(ctx_gguf, datasets_key);
         imatrix.datasets.reserve(imatrix.datasets.size() + n);
         for (int64_t i = 0; i < n; ++i) {
@@ -143,6 +144,13 @@ bool common_imatrix_load(const std::string & fname, common_imatrix & imatrix) {
             return false;
         }
 
+        if (in_sum2->type != GGML_TYPE_F32 || counts->type != GGML_TYPE_F32) {
+            LOG_ERR("%s: sums and counts for %s must be F32\n", __func__, name.c_str());
+            gguf_free(ctx_gguf);
+            ggml_free(ctx);
+            return false;
+        }
+
         auto & e = imatrix.entries[name];
 
         const int64_t nval    = ggml_nelements(in_sum2);

common/jinja/caps.cpp

@@ -20,7 +20,7 @@ namespace jinja {
 
 using caps_json_fn = std::function<json()>;
 using caps_ctx_fn = std::function<void(context &)>;
-using caps_analyze_fn = std::function<void(bool, value &, value &, const std::string &)>;
+using caps_analyze_fn = std::function<void(context &, bool, value &, value &, const std::string &)>;
 
 void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
     ctx.set_val("preserve_thinking",         mk_val<value_bool>(enabled));
@@ -29,6 +29,12 @@ void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled) {
     ctx.set_val("drop_thinking",            mk_val<value_bool>(!enabled));
 }
 
+void caps_apply_reasoning_effort(jinja::context & ctx, const std::string & effort) {
+    value var = mk_val<value_string>(effort); // bind to the same value for stats
+    ctx.set_val("reasoning_effort",   var);
+    ctx.set_val("reasoning_strength", var);
+}
+
 static void caps_try_execute(jinja::program & prog,
                              const caps_json_fn & messages_fn,
                              const caps_ctx_fn & ctx_fn,
@@ -65,7 +71,7 @@ static void caps_try_execute(jinja::program & prog,
         // ignore exceptions during capability analysis
     }
 
-    analyze_fn(success, messages, tools, result);
+    analyze_fn(ctx, success, messages, tools, result);
 }
 
 // for debugging only
@@ -90,6 +96,7 @@ std::map<std::string, bool> caps::to_map() const {
         {"supports_parallel_tool_calls", supports_parallel_tool_calls},
         {"supports_system_role", supports_system_role},
         {"supports_preserve_reasoning", supports_preserve_reasoning},
+        {"supports_reasoning_effort", supports_reasoning_effort},
         {"supports_object_arguments", supports_object_arguments},
     };
 }
@@ -127,7 +134,7 @@ caps caps_get(jinja::program & prog) {
         },
         nullptr, // ctx_fn
         nullptr, // tools_fn
-        [&](bool success, value & messages, value &, const std::string &) {
+        [&](context &, bool success, value & messages, value &, const std::string &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (has_op(content, "selectattr") || has_op(content, "array_access")) {
@@ -161,7 +168,7 @@ caps caps_get(jinja::program & prog) {
         },
         nullptr, // ctx_fn
         nullptr, // tools_fn
-        [&](bool, value & messages, value &, const std::string &) {
+        [&](context &, bool, value & messages, value &, const std::string &) {
             auto & content = messages->at(0)->at("content");
             caps_print_stats(content, "messages[0].content");
             if (!content->stats.used) {
@@ -237,7 +244,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value & tools, const std::string &) {
+        [&](context &, bool success, value & messages, value & tools, const std::string &) {
             if (!success) {
                 return; // Nothing can be inferred
             }
@@ -330,7 +337,7 @@ caps caps_get(jinja::program & prog) {
                     },
                 });
             },
-            [&](bool success, value & messages, value & tools, const std::string &) {
+            [&](context &, bool success, value & messages, value & tools, const std::string &) {
                 if (!success) {
                     result.supports_tool_calls = false;
                     result.supports_tools = false;
@@ -432,7 +439,7 @@ caps caps_get(jinja::program & prog) {
                 },
             });
         },
-        [&](bool success, value & messages, value &, const std::string &) {
+        [&](context &, bool success, value & messages, value &, const std::string &) {
             if (!success) {
                 result.supports_parallel_tool_calls = false;
                 return;
@@ -485,10 +492,11 @@ caps caps_get(jinja::program & prog) {
             });
         },
         [&](context & ctx) {
+            ctx.set_val("enable_thinking", mk_val<value_bool>(true));
             caps_apply_preserve_reasoning(ctx, true);
         },
         nullptr, // tools_fn
-        [&](bool, value &, value &, const std::string & output) {
+        [&](context &, bool, value &, value &, const std::string & output) {
             // note: we cannot use stats here because the reasoning_content may be used for "if" condition test, but not actually outputted in the final result
             if (output.find(reasoning_placeholder) != std::string::npos) {
                 result.supports_preserve_reasoning = true;
@@ -496,6 +504,32 @@ caps caps_get(jinja::program & prog) {
         }
     );
 
+    JJ_DEBUG("%s\n", ">>> Running capability check: reasoning effort");
+
+    // case: reasoning effort level
+    caps_try_execute(
+        prog,
+        [&]() {
+            // messages
+            return json::array({
+                {
+                    {"role", "user"},
+                    {"content", "User message"}
+                },
+            });
+        },
+        [&](context & ctx) {
+            ctx.set_val("enable_thinking", mk_val<value_bool>(true));
+            caps_apply_reasoning_effort(ctx, "low");
+        },
+        nullptr, // tools_fn
+        [&](context & ctx, bool, value &, value &, const std::string &) {
+            value effort = ctx.get_val("reasoning_effort");
+            caps_print_stats(effort, "reasoning_effort");
+            result.supports_reasoning_effort = effort->stats.used;
+        }
+    );
+
     JJ_DEBUG("%s\n", result.to_string().c_str());
 
     return result;

common/jinja/caps.h

@@ -16,6 +16,9 @@ struct caps {
     // supports preserve reasoning trace in the full history, not just the last assistant message
     bool supports_preserve_reasoning = false;
 
+    // supports reasoning effort levels
+    bool supports_reasoning_effort = false;
+
     // one of the 2 content capabilities must be true
     bool supports_string_content = true;
     bool supports_typed_content = false;
@@ -32,5 +35,6 @@ struct caps {
 caps caps_get(jinja::program & prog);
 
 void caps_apply_preserve_reasoning(jinja::context & ctx, bool enabled);
+void caps_apply_reasoning_effort(jinja::context & ctx, const std::string & effort);
 
 } // namespace jinja

common/jinja/runtime.cpp

@@ -266,7 +266,7 @@ value binary_expression::execute_impl(context & ctx) {
             return res;
         }
         for (int64_t i = 0; i < repeat; ++i) {
-            res->val_str = res->val_str.append(str);
+            res->val_str.append(str);
         }
         return res;
     }

common/jinja/runtime.h

@@ -763,14 +763,22 @@ struct runtime {
         gather_string_parts_recursive(val, parts);
         // join consecutive parts with the same type
         auto & p = parts->val_str.parts;
-        for (size_t i = 1; i < p.size(); ) {
-            if (p[i].is_input == p[i - 1].is_input) {
-                p[i - 1].val += p[i].val;
-                p.erase(p.begin() + i);
+        if (p.empty()) {
+            return parts;
+        }
+        size_t w = 0;
+        for (size_t r = 1; r < p.size(); r++) {
+            if (p[w].is_input == p[r].is_input) {
+                p[w].val += p[r].val;
             } else {
-                i++;
+                w++;
+                if (w != r) {
+                    // the guard is needed, self-move leaves the string in an unspecified state
+                    p[w] = std::move(p[r]);
+                }
             }
         }
+        p.resize(w + 1);
         return parts;
     }
 

common/jinja/string.cpp

@@ -103,7 +103,7 @@ void string::mark_input_based_on(const string & other) {
     }
 }
 
-string string::append(const string & other) {
+string & string::append(const string & other) {
     for (const auto & part : other.parts) {
         parts.push_back(part);
     }

common/jinja/string.h

@@ -47,7 +47,7 @@ struct string {
     // mark this string as input if other has ALL parts as input
     void mark_input_based_on(const string & other);
 
-    string append(const string & other);
+    string & append(const string & other);
 
     // in-place transformations
 

common/llguidance.cpp

@@ -116,6 +116,8 @@ static llama_sampler_i llama_sampler_llg_i = {
     /* .backend_accept    = */ NULL,
     /* .backend_apply     = */ NULL,
     /* .backend_set_input = */ NULL,
+    /* .backend_reset     = */ NULL,
+    /* .copy_state        = */ NULL,
 };
 
 static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,

common/peg-parser.cpp

@@ -570,23 +570,34 @@ struct parser_executor {
     }
 
     static common_peg_parse_result handle_escape_sequence(common_peg_parse_context & ctx, size_t start, size_t & pos, const char delimiter) {
+        auto save = pos;
+
         ++pos; // consume '\'
         if (pos >= ctx.input.size()) {
             if (!ctx.is_lenient()) {
                 return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
             }
+            pos = save; // suppress unmatched '\'
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos);
         }
 
         char c = ctx.input[pos];
+
         if (c == delimiter || c == '\\' || c == '/' || c == 'b' || c == 'f' || c == 'n' || c == 'r' || c == 't') {
             ++pos;
             return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_SUCCESS, start, pos);
-        } else if (c == 'u') {
-            return handle_unicode_escape(ctx, start, pos);
-        } else {
-            return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
         }
+
+        if (c == 'u') {
+            auto result = handle_unicode_escape(ctx, start, pos);
+            if (result.need_more_input()) {
+                pos = save; // suppress incomplete sequence
+                return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT, start, pos);
+            }
+            return result;
+        }
+
+        return common_peg_parse_result(COMMON_PEG_PARSE_RESULT_FAIL, start);
     }
 
     static common_peg_parse_result handle_unicode_escape(common_peg_parse_context & ctx, size_t start, size_t & pos) {

common/preset.cpp

@@ -322,6 +322,8 @@ common_presets common_preset_context::load_from_ini(const std::string & path, co
                     preset.options[opt] = value;
                 }
                 LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
+            } else if (ignore_unknown_keys) {
+                LOG_WRN("ignoring option '%s' from %s: not supported by this program\n", key.c_str(), path.c_str());
             } else {
                 throw std::runtime_error(string_format(
                     "option '%s' not recognized in preset '%s'",
@@ -363,8 +365,25 @@ struct local_model {
     std::string name;
     std::string path;
     std::string path_mmproj;
+    std::string path_draft;
 };
 
+// TODO @ngxson: handle "eagle3-" when it's supported by common_speculative_types_from_gguf()
+static const char * draft_prefixes[] = { "mtp-", "dspark-", "dflash-" };
+
+static bool is_mmproj_file(const std::string & fname) {
+    return fname.find("mmproj") != std::string::npos;
+}
+
+static bool is_draft_file(const std::string & fname) {
+    for (const auto & prefix : draft_prefixes) {
+        if (fname.rfind(prefix, 0) == 0) {
+            return true;
+        }
+    }
+    return false;
+}
+
 common_presets common_preset_context::load_from_models_dir(const std::string & models_dir) const {
     if (!std::filesystem::exists(models_dir) || !std::filesystem::is_directory(models_dir)) {
         throw std::runtime_error(string_format("error: '%s' does not exist or is not a directory\n", models_dir.c_str()));
@@ -376,10 +395,15 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         common_file_info model_file;
         common_file_info first_shard_file;
         common_file_info mmproj_file;
+        common_file_info draft_file;
         for (const auto & file : files) {
             if (string_ends_with(file.name, ".gguf")) {
-                if (file.name.find("mmproj") != std::string::npos) {
+                if (is_mmproj_file(file.name)) {
                     mmproj_file = file;
+                } else if (is_draft_file(file.name)) {
+                    if (draft_file.path.empty()) {
+                        draft_file = file; // first sidecar found wins
+                    }
                 } else if (file.name.find("-00001-of-") != std::string::npos) {
                     first_shard_file = file;
                 } else {
@@ -391,7 +415,8 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         local_model model{
             /* name        */ name,
             /* path        */ first_shard_file.path.empty() ? model_file.path : first_shard_file.path,
-            /* path_mmproj */ mmproj_file.path // can be empty
+            /* path_mmproj */ mmproj_file.path, // can be empty
+            /* path_draft  */ draft_file.path   // can be empty
         };
         if (!model.path.empty()) {
             models.push_back(model);
@@ -403,13 +428,17 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         if (file.is_dir) {
             scan_subdir(file.path, file.name);
         } else if (string_ends_with(file.name, ".gguf")) {
+            if (is_mmproj_file(file.name) || is_draft_file(file.name)) {
+                continue; // companion file, cannot be loaded as a model on its own
+            }
             // single file model
             std::string name = file.name;
             string_replace_all(name, ".gguf", "");
             local_model model{
                 /* name        */ name,
                 /* path        */ file.path,
-                /* path_mmproj */ ""
+                /* path_mmproj */ "",
+                /* path_draft  */ ""
             };
             models.push_back(model);
         }
@@ -424,6 +453,9 @@ common_presets common_preset_context::load_from_models_dir(const std::string & m
         if (!model.path_mmproj.empty()) {
             preset.set_option(*this, "LLAMA_ARG_MMPROJ", model.path_mmproj);
         }
+        if (!model.path_draft.empty()) {
+            preset.set_option(*this, "LLAMA_ARG_SPEC_DRAFT_MODEL", model.path_draft);
+        }
         out[preset.name] = preset;
     }
 

common/preset.h

@@ -59,6 +59,10 @@ struct common_preset_context {
     bool filter_allowed_keys = false;
     std::set<std::string> allowed_keys;
 
+    // if true, options unknown to the current example are skipped instead of being an error
+    // used for config files shared by all binaries, where each binary only knows a subset of options
+    bool ignore_unknown_keys = false;
+
     // if only_remote_allowed is true, only accept whitelisted keys
     common_preset_context(llama_example ex);
 

common/reasoning-budget.cpp

@@ -217,6 +217,8 @@ static struct llama_sampler_i common_reasoning_budget_i = {
     /* .backend_accept    = */ nullptr,
     /* .backend_apply     = */ nullptr,
     /* .backend_set_input = */ nullptr,
+    /* .backend_reset     = */ nullptr,
+    /* .copy_state        = */ nullptr,
 };
 
 static struct llama_sampler * common_reasoning_budget_clone(const struct llama_sampler * smpl) {

common/sampling.cpp

@@ -184,9 +184,21 @@ std::string common_params_sampling::print() const {
     return std::string(result);
 }
 
-struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params) {
+struct common_sampler * common_sampler_init(
+        const struct llama_model * model,
+        struct common_params_sampling & params) {
+    if (!std::isfinite(params.penalty_repeat) ||
+        params.penalty_repeat <= 0.0f ||
+        !std::isfinite(1.0f/params.penalty_repeat)) {
+        throw std::invalid_argument("penalty_repeat must be finite and greater than 0");
+    }
+    if (!std::isfinite(params.penalty_freq)) {
+        throw std::invalid_argument("penalty_freq must be finite");
+    }
+    if (!std::isfinite(params.penalty_present)) {
+        throw std::invalid_argument("penalty_present must be finite");
+    }
     const llama_vocab * vocab = llama_model_get_vocab(model);
-
     llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
 
     lparams.no_perf = params.no_perf;
@@ -338,7 +350,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
                         for (const auto & str : params.dry_sequence_breakers) {
                             c_breakers.push_back(str.c_str());
                         }
-                        samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
+                        samplers.push_back(llama_sampler_init_dry(vocab, params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
                     }
                     break;
                 case COMMON_SAMPLER_TYPE_TOP_K:
@@ -366,7 +378,7 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, st
                     samplers.push_back(llama_sampler_init_infill(vocab));
                     break;
                 case COMMON_SAMPLER_TYPE_PENALTIES:
-                    samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
+                    samplers.push_back(llama_sampler_init_penalties(llama_vocab_n_tokens(vocab), params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
                     break;
                 case COMMON_SAMPLER_TYPE_ADAPTIVE_P:
                     // the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects
@@ -506,6 +518,26 @@ struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
     };
 }
 
+void common_sampler_copy(const common_sampler * src, common_sampler * dst) {
+    if (!src || !dst || src == dst) {
+        return;
+    }
+
+    GGML_ASSERT((src->grmr == nullptr) == (dst->grmr == nullptr));
+    GGML_ASSERT((src->rbudget == nullptr) == (dst->rbudget == nullptr));
+
+    llama_sampler_copy(src->grmr,    dst->grmr);
+    llama_sampler_copy(src->rbudget, dst->rbudget);
+    llama_sampler_copy(src->chain,   dst->chain);
+
+    dst->params     = src->params;
+    dst->prev       = src->prev;
+    dst->cur        = src->cur;
+    dst->cur_p      = src->cur_p;
+    dst->cur_p.data = src->cur_p.data ? dst->cur.data() : nullptr; // re-point to dst's buffer
+    dst->t_total_us = src->t_total_us;
+}
+
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
     // TODO: measure grammar performance
 

common/sampling.h

@@ -37,7 +37,9 @@ struct common_sampler;
 // llama_sampler API overloads
 
 // note: can mutate params in some cases
-struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params);
+struct common_sampler * common_sampler_init(
+        const struct llama_model * model,
+        struct common_params_sampling & params);
 
 void common_sampler_free(struct common_sampler * gsmpl);
 
@@ -45,6 +47,7 @@ void common_sampler_free(struct common_sampler * gsmpl);
 void                    common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool is_generated);
 void                    common_sampler_reset (struct common_sampler * gsmpl);
 struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
+void                    common_sampler_copy  (const struct common_sampler * src, struct common_sampler * dst);
 
 // arguments can be nullptr to skip printing
 void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);

common/speculative.cpp

@@ -2,6 +2,7 @@
 
 #include "common.h"
 #include "ggml.h"
+#include "ggml-cpp.h"
 #include "llama.h"
 #include "log.h"
 #include "ngram-cache.cpp"
@@ -171,12 +172,6 @@ struct common_speculative_impl {
     // (optional) serialize/restore per-seq internal state (e.g. eagle3's deferred boundary).
     virtual bool get_state(llama_seq_id /*seq_id*/, std::vector<uint8_t> & /*data*/) const { return false; }
     virtual void set_state(llama_seq_id /*seq_id*/, const std::vector<uint8_t> & /*data*/) {}
-
-    // true if this implementation requires the target context to extract post-norm embeddings
-    virtual bool need_embd() const = 0;
-
-    // true if this implementation requires the target context to extract pre-norm embeddings
-    virtual bool need_embd_nextn() const { return false; }
 };
 
 struct common_speculative_impl_draft_simple : public common_speculative_impl {
@@ -193,6 +188,10 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
         auto * ctx_dft = this->params.ctx_dft;
         auto * ctx_tgt = this->params.ctx_tgt;
 
+        if (!ctx_dft) {
+            throw std::runtime_error("draft-simple requires a draft context");
+        }
+
         SPC_TRC("%s", "adding speculative implementation 'draft-simple'\n");
         SPC_TRC("- n_max=%d, n_min=%d, p_min=%f\n", this->params.n_max, this->params.n_min, this->params.p_min);
         SPC_TRC("- gpu_layers=%d, cache_k=%s, cache_v=%s, ctx_tgt=%s, ctx_dft=%s, devices=[%s]\n",
@@ -385,10 +384,6 @@ struct common_speculative_impl_draft_simple : public common_speculative_impl {
     void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
-
-    bool need_embd() const override {
-        return false;
-    }
 };
 
 
@@ -907,10 +902,6 @@ struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
         pending_g_last[seq_id].resize(n_embd_dec);
         std::memcpy(pending_g_last[seq_id].data(), data.data() + sizeof(llama_pos), (size_t) n_embd_dec * sizeof(float));
     }
-
-    bool need_embd() const override {
-        return false;
-    }
 };
 
 // DFlash: block-diffusion drafting with a draft-side KV cache injection
@@ -922,6 +913,9 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
 
     std::vector<common_sampler_ptr> smpls;
 
+    // backend sampler chain per seq, attached to ctx_dft
+    std::vector<llama_sampler *> backend_chains;
+
     int32_t n_embd_dec = 0;  // draft hidden size
     int32_t n_embd_enc = 0;  // target_layer_ids_n * target_hidden_size
     int32_t n_embd_tgt = 0;  // target model hidden size
@@ -995,6 +989,22 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
             s.reset(common_sampler_init(model_dft, sparams));
         }
 
+        // offload draft sampling to the backend
+        backend_chains.assign(n_seq, nullptr);
+        if (this->params.backend_sampling) {
+            for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+                llama_sampler * chain = llama_sampler_chain_init(llama_sampler_chain_default_params());
+                llama_sampler_chain_add(chain, llama_sampler_init_top_k(10));
+
+                if (!llama_set_sampler(ctx_dft, seq_id, chain)) {
+                    SPC_WRN("backend offload failed for seq_id=%d; using CPU sampler\n", (int) seq_id);
+                    llama_sampler_free(chain);
+                    chain = nullptr;
+                }
+                backend_chains[seq_id] = chain;
+            }
+        }
+
         // turn on extraction of the target layers' input embeddings
         for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
             llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
@@ -1005,6 +1015,18 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
     }
 
     ~common_speculative_impl_draft_dflash() override {
+        auto * ctx_dft = this->params.ctx_dft;
+        for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) backend_chains.size(); ++seq_id) {
+            if (backend_chains[seq_id] == nullptr) {
+                continue;
+            }
+            if (ctx_dft) {
+                llama_set_sampler(ctx_dft, seq_id, nullptr);
+            }
+            llama_sampler_free(backend_chains[seq_id]);
+        }
+        backend_chains.clear();
+
         llama_batch_free(batch);
         llama_batch_free(batch_inject);
     }
@@ -1032,7 +1054,14 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
             return true;
         }
 
-        if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+        // Target prefill may contain token IDs or multimodal embeddings. Both
+        // produce the target-layer features used to seed the draft KV cache, so
+        // skipping the embedding batches leaves a hole in the draft's cache and
+        // the next injection fails to initialize.
+        // TODO: revisit after https://github.com/ggml-org/llama.cpp/pull/24669 is merged
+        const bool has_tokens     = batch_in.token != nullptr;
+        const bool has_embeddings = batch_in.embd  != nullptr;
+        if (has_tokens == has_embeddings) {
             return true;
         }
 
@@ -1240,10 +1269,6 @@ struct common_speculative_impl_draft_dflash : public common_speculative_impl {
     void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
-
-    bool need_embd() const override {
-        return false;
-    }
 };
 
 struct common_speculative_impl_draft_mtp : public common_speculative_impl {
@@ -1291,7 +1316,7 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         GGML_ASSERT(ctx_tgt && ctx_dft && "MTP requires ctx_tgt and ctx_dft to be set");
 
         n_embd = llama_model_n_embd_out(llama_get_model(ctx_dft));
-        GGML_ASSERT(n_embd == llama_model_n_embd(llama_get_model(ctx_tgt)) &&
+        GGML_ASSERT(n_embd == llama_model_n_embd_out(llama_get_model(ctx_tgt)) &&
                 "MTP input row width must match the target h_nextn width");
         n_mtp_layers = std::max(1, (int) llama_model_n_layer_nextn(llama_get_model(ctx_dft)));
 
@@ -1682,14 +1707,6 @@ struct common_speculative_impl_draft_mtp : public common_speculative_impl {
         const size_t row_bytes = (size_t) n_embd * sizeof(float);
         std::memcpy(pending_h[seq_id].data(), verify_h[seq_id].data() + (size_t) i_h * n_embd, row_bytes);
     }
-
-    bool need_embd() const override {
-        return false;
-    }
-
-    bool need_embd_nextn() const override {
-        return true;
-    }
 };
 
 // state of self-speculation (simple implementation, not ngram-map)
@@ -1736,10 +1753,6 @@ struct common_speculative_impl_ngram_simple : public common_speculative_impl {
     void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
-
-    bool need_embd() const override {
-        return false;
-    }
 };
 
 struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
@@ -1794,10 +1807,6 @@ struct common_speculative_impl_ngram_map_k : public common_speculative_impl {
 
         common_ngram_map_accept(config[seq_id], n_accepted);
     }
-
-    bool need_embd() const override {
-        return false;
-    }
 };
 
 struct common_speculative_impl_ngram_mod : public common_speculative_impl {
@@ -1973,10 +1982,6 @@ struct common_speculative_impl_ngram_mod : public common_speculative_impl {
             }
         }
     }
-
-    bool need_embd() const override {
-        return false;
-    }
 };
 
 struct common_speculative_impl_ngram_cache : public common_speculative_impl {
@@ -2116,10 +2121,6 @@ struct common_speculative_impl_ngram_cache : public common_speculative_impl {
     void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
         // noop
     }
-
-    bool need_embd() const override {
-        return false;
-    }
 };
 
 struct common_speculative {
@@ -2227,6 +2228,43 @@ common_speculative_type common_speculative_type_from_name(const std::string & na
     return it->second;
 }
 
+std::vector<common_speculative_type> common_speculative_types_from_gguf(const std::string & path) {
+    struct gguf_init_params gguf_params = {
+        /* .no_alloc = */ true,
+        /* .ctx      = */ nullptr,
+    };
+
+    gguf_context_ptr gguf_ctx(gguf_init_from_file(path.c_str(), gguf_params));
+    if (!gguf_ctx) {
+        return {};
+    }
+
+    const int64_t arch_id = gguf_find_key(gguf_ctx.get(), "general.architecture");
+    if (arch_id < 0 || gguf_get_kv_type(gguf_ctx.get(), arch_id) != GGUF_TYPE_STRING) {
+        return {};
+    }
+
+    const std::string arch = gguf_get_val_str(gguf_ctx.get(), arch_id);
+    if (arch != "dflash") {
+        const uint32_t block_count = gguf_get_val_u32(gguf_ctx.get(), gguf_find_key(gguf_ctx.get(), (arch + ".block_count").c_str()));
+
+        if (gguf_find_tensor(gguf_ctx.get(), ("blk." + std::to_string(block_count - 1) + ".nextn.eh_proj.weight").c_str()) >= 0) {
+            return { COMMON_SPECULATIVE_TYPE_DRAFT_MTP };
+        }
+
+        return {};
+    }
+
+    // the Markov head distinguishes draft-dspark from draft-dflash
+    const auto type = gguf_find_tensor(gguf_ctx.get(), "markov_w1.weight") >= 0
+                    ? COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK
+                    : COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH;
+
+    SPC_INF("auto-detected speculative type '%s' from the draft model metadata\n", common_speculative_type_to_str(type).c_str());
+
+    return { type };
+}
+
 static uint32_t common_get_enabled_speculative_configs(const std::vector<common_speculative_type> & configs) {
     uint32_t result = 0;
     for (size_t i = 0; i < configs.size(); i++) {
@@ -2292,6 +2330,24 @@ common_params common_base_params_to_speculative(const common_params & params) {
     result.cache_type_k  = params_spec.cache_type_k;
     result.cache_type_v  = params_spec.cache_type_v;
     result.n_outputs_max = params.n_parallel;
+    result.n_outputs_max_per_seq = 1;
+
+    // dflash/dspark decode the whole noise block in a single pass and sample every block position on the backend
+    // TODO: refactor such properties to be announced by the speculative types
+    //       something like `struct common_speculative_type_props common_speculative_type_get_props(...);`
+    const bool has_block_draft = std::any_of(
+        params.speculative.types.begin(), params.speculative.types.end(),
+        [](common_speculative_type t) {
+            return t == COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH || t == COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK;
+        });
+    if (has_block_draft) {
+        // per-seq output positions: DFlash decodes anchor + n_max masks (n_max + 1); DSpark n_max -> +1 covers both
+        const int32_t per_seq = std::max(1, params_spec.n_max + 1);
+        result.n_outputs_max = params.n_parallel * per_seq;
+        if (params_spec.backend_sampling) {
+            result.n_outputs_max_per_seq = per_seq;
+        }
+    }
 
     return result;
 }
@@ -2314,7 +2370,6 @@ common_speculative_init_result::common_speculative_init_result(
     const bool spec_mtp = std::find(params.speculative.types.begin(),
                                     params.speculative.types.end(),
                                     COMMON_SPECULATIVE_TYPE_DRAFT_MTP) != params.speculative.types.end();
-    GGML_ASSERT(has_draft || spec_mtp);
 
     auto mparams = common_model_params_to_llama(params);
     auto cparams = common_context_params_to_llama(params);
@@ -2377,6 +2432,17 @@ common_speculative_init_result_ptr common_speculative_init_from_params(common_pa
     return std::make_unique<common_speculative_init_result>(params, model_tgt, ctx_tgt);
 }
 
+common_speculative_output_limits common_speculative_get_output_limits(
+        int32_t n_batch, int32_t n_parallel, int32_t n_draft) {
+    const int64_t per_seq = 1 + (int64_t) std::max(0, n_draft);
+    const int64_t total   = (int64_t) n_parallel * per_seq;
+
+    return {
+        /* .total   = */ (int32_t) std::min<int64_t>(n_batch, total),
+        /* .per_seq = */ (int32_t) std::min<int64_t>(n_batch, per_seq),
+    };
+}
+
 // initialization of the speculative decoding system
 //
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq) {
@@ -2385,57 +2451,28 @@ common_speculative * common_speculative_init(common_params_speculative & params,
     {
         uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types);
 
-        bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
-        bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
-        bool has_draft_mtp    = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP))    && params.draft.ctx_dft != nullptr;
-        bool has_draft_dflash = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH)) && params.draft.ctx_dft != nullptr;
-        bool has_draft_dspark = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK)) && params.draft.ctx_dft != nullptr;
-
-
-
-        bool has_ngram_cache   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE));
-        bool has_ngram_simple  = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE));
-        bool has_ngram_map_k   = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K));
-        bool has_ngram_map_k4v = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V));
-        bool has_ngram_mod     = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MOD));
+        auto add_config_if_enabled = [&](common_speculative_type type, bool available = true) {
+            if (available && (enabled_configs & (1u << type))) {
+                configs.emplace_back(type, params);
+            }
+        };
 
         // when adding a new type - update here the logic above
         static_assert(COMMON_SPECULATIVE_TYPE_COUNT == 11);
 
         // this list here defines the priority of the speculators
         // the one with highest priority are listed first
-        if (has_ngram_simple) {
-            // This implementation can guess a lot of tokens without any draft model.
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE, params));
-        }
-        if (has_ngram_map_k) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K, params));
-        }
-        if (has_ngram_map_k4v) {
-            // This implementation can guess tokens with high acceptance rate but is more expensive.
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V, params));
-        }
-        if (has_ngram_mod) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_MOD, params));
-        }
-        if (has_ngram_cache) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE, params));
-        }
-        if (has_draft_simple) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE, params));
-        }
-        if (has_draft_eagle3) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params));
-        }
-        if (has_draft_mtp) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_MTP, params));
-        }
-        if (has_draft_dflash) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params));
-        }
-        if (has_draft_dspark) {
-            configs.push_back(common_speculative_config(COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, params));
-        }
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K4V);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_MOD);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_NGRAM_CACHE);
+
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, params.draft.ctx_dft != nullptr);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_MTP,    params.draft.ctx_dft != nullptr);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_DFLASH, params.draft.ctx_dft != nullptr);
+        add_config_if_enabled(COMMON_SPECULATIVE_TYPE_DRAFT_DSPARK, params.draft.ctx_dft != nullptr);
     }
 
     std::vector<std::unique_ptr<common_speculative_impl>> impls = {};
@@ -2570,34 +2607,6 @@ bool common_speculative_process(common_speculative * spec, const llama_batch & b
     return result;
 }
 
-bool common_speculative_need_embd(common_speculative * spec) {
-    if (spec == nullptr) {
-        return false;
-    }
-
-    for (auto & impl : spec->impls) {
-        if (impl->need_embd()) {
-            return true;
-        }
-    }
-
-    return false;
-}
-
-bool common_speculative_need_embd_nextn(common_speculative * spec) {
-    if (spec == nullptr) {
-        return false;
-    }
-
-    for (auto & impl : spec->impls) {
-        if (impl->need_embd_nextn()) {
-            return true;
-        }
-    }
-
-    return false;
-}
-
 void common_speculative_draft(common_speculative * spec) {
     if (spec == nullptr) {
         return;
@@ -2682,7 +2691,10 @@ void common_speculative_draft(common_speculative * spec) {
 void common_speculative_accept(common_speculative * spec, llama_seq_id seq_id, uint16_t n_accepted) {
     common_speculative_impl * impl = spec->impl_last[seq_id];
 
-    GGML_ASSERT(impl);
+    if (impl == nullptr) {
+        GGML_ASSERT(n_accepted == 0);
+        return;
+    }
 
     {
         common_time_meas tm(impl->t_accept_us, !impl->gen_perf);

common/speculative.h

@@ -14,6 +14,9 @@ const char * common_speculative_all_types_str();
 // parse user provided types
 std::vector<enum common_speculative_type> common_speculative_types_from_names(const std::vector<std::string> & names);
 
+// infer the spec types from the GGUF metadata of a draft model; empty if unknown
+std::vector<enum common_speculative_type> common_speculative_types_from_gguf(const std::string & path);
+
 // convert string to type
 enum common_speculative_type common_speculative_type_from_name(const std::string & name);
 
@@ -25,6 +28,15 @@ int32_t common_speculative_n_max(const common_params_speculative * spec);
 
 common_params common_base_params_to_speculative(const common_params & params);
 
+struct common_speculative_output_limits {
+    int32_t total;
+    int32_t per_seq;
+};
+
+// return the output limits needed for speculative decoding
+common_speculative_output_limits common_speculative_get_output_limits(
+        int32_t n_batch, int32_t n_parallel, int32_t n_draft);
+
 common_speculative * common_speculative_init(common_params_speculative & params, uint32_t n_seq);
 
 void common_speculative_free(common_speculative * spec);
@@ -58,12 +70,6 @@ void common_speculative_begin(common_speculative * spec, llama_seq_id seq_id, co
 // process the batch and update the internal state of the speculative context
 bool common_speculative_process(common_speculative * spec, const llama_batch & batch);
 
-// true if any implementation requires target post-norm embeddings to be extracted
-bool common_speculative_need_embd(common_speculative * spec);
-
-// true if any implementation requires target nextn embeddings to be extracted
-bool common_speculative_need_embd_nextn(common_speculative * spec);
-
 // generate drafts for the sequences specified with `common_speculative_get_draft_params`
 void common_speculative_draft(common_speculative * spec);
 

conversion/__init__.py

@@ -55,6 +55,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "DFlashDraftModel": "qwen",
     "Qwen3DSparkModel": "qwen",
     "DeepseekV4ForCausalLM": "deepseek",
+    "DeepseekV4DSparkModel": "deepseek",
     "DistilBertForMaskedLM": "bert",
     "DistilBertForSequenceClassification": "bert",
     "DistilBertModel": "bert",
@@ -69,6 +70,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "Exaone4ForCausalLM": "exaone",
     "ExaoneForCausalLM": "exaone",
     "ExaoneMoEForCausalLM": "exaone",
+    "ExaoneMoeForCausalLM": "exaone",
     "FalconForCausalLM": "falcon",
     "FalconH1ForCausalLM": "falcon_h1",
     "FalconMambaForCausalLM": "mamba",
@@ -101,6 +103,7 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "GraniteMoeForCausalLM": "granite",
     "GraniteMoeHybridForCausalLM": "granite",
     "GraniteMoeSharedForCausalLM": "granite",
+    "GraniteSwitchForCausalLM": "granite",
     "GraniteSpeechForConditionalGeneration": "granite",
     "GraniteSpeechPlusForConditionalGeneration": "granite",
     "Grok1ForCausalLM": "grok",
@@ -158,6 +161,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "MiniCPM3ForCausalLM": "minicpm",
     "MiniCPMForCausalLM": "minicpm",
     "MiniCPMV4_6ForConditionalGeneration": "minicpm",
+    "MiniMaxText01ForCausalLM": "minimax",
+    "MiniMaxM1ForCausalLM": "minimax",
     "MiniMaxM2ForCausalLM": "minimax",
     "MiniMaxM3SparseForCausalLM": "minimax",
     "MiniMaxM3SparseForConditionalGeneration": "minimax",
@@ -180,6 +185,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "Olmo3ForCausalLM": "olmo",
     "OlmoForCausalLM": "olmo",
     "OlmoeForCausalLM": "olmo",
+    "MuseGlimmerAssistantModel": "muse_glimmer",
+    "MuseGlimmerForConditionalGeneration": "muse_glimmer",
     "OpenELMForCausalLM": "openelm",
     "OrionForCausalLM": "orion",
     "PLMForCausalLM": "plm",
@@ -209,6 +216,8 @@ TEXT_MODEL_MAP: dict[str, str] = {
     "Qwen3MoeForCausalLM": "qwen",
     "Qwen3NextForCausalLM": "qwen",
     "Qwen3OmniMoeForConditionalGeneration": "qwen3vl",
+    "PocketTTSModel": "pockettts",
+    "Qwen3TTSForConditionalGeneration": "qwen3tts",
     "Qwen3VLForConditionalGeneration": "qwen3vl",
     "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
     "Qwen3_5ForCausalLM": "qwen",
@@ -294,6 +303,7 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "MiniCPMV4_6ForConditionalGeneration": "minicpm",
     "Mistral3ForConditionalGeneration": "llava",
     "NemotronH_Nano_VL_V2": "nemotron",
+    "MuseGlimmerForConditionalGeneration": "muse_glimmer",
     "PaddleOCRVisionModel": "ernie",
     "Phi4ForCausalLMV": "phi",
     "Qwen2AudioForConditionalGeneration": "ultravox",
@@ -303,6 +313,8 @@ MMPROJ_MODEL_MAP: dict[str, str] = {
     "Qwen2_5_VLForConditionalGeneration": "qwenvl",
     "Qwen3ASRForConditionalGeneration": "qwen3vl",
     "Qwen3OmniMoeForConditionalGeneration": "qwen3vl",
+    "PocketTTSModel": "pockettts",
+    "Qwen3TTSForConditionalGeneration": "qwen3tts",
     "Qwen3VLForConditionalGeneration": "qwen3vl",
     "Qwen3VLMoeForConditionalGeneration": "qwen3vl",
     "Qwen3_5ForConditionalGeneration": "qwen3vl",

conversion/base.py

@@ -58,6 +58,11 @@ logger = logging.getLogger("hf-to-gguf")
 AnyModel = TypeVar("AnyModel", bound="type[ModelBase]")
 
 
+# for checkpoints that ship no config.json, we will try to provide a synthetic one
+HparamsMatcher = Callable[[Path], bool]
+HparamsLoader = Callable[[Path], dict[str, Any]]
+
+
 class SentencePieceTokenTypes(IntEnum):
     NORMAL = 1
     UNKNOWN = 2
@@ -77,6 +82,7 @@ class ModelBase:
         ModelType.TEXT: {},
         ModelType.MMPROJ: {},
     }
+    _hparams_loaders: list[tuple[HparamsMatcher, HparamsLoader]] = []
 
     dir_model: Path
     ftype: gguf.LlamaFileType
@@ -823,7 +829,7 @@ class ModelBase:
             elif any(str(v.get("quant_algo")).endswith("NVFP4") for v in quant_layers.values() if isinstance(v, dict)):
                 quant_algo = "NVFP4"
 
-        self._is_nvfp4 = quant_algo == "NVFP4"
+        self._is_nvfp4 = quant_algo in ("NVFP4", "W4A16_NVFP4")
         self._is_mxfp4 = quant_method == "mxfp4"
 
         # NVFP4 weights are repacked and written directly to gguf_writer.
@@ -1040,6 +1046,24 @@ class ModelBase:
 
         return part_names
 
+    @staticmethod
+    def load_hparams_guess(dir_model: Path) -> dict[str, Any] | None:
+        # some models ship no config.json, will try to guess them
+        from conversion import load_all_models
+        load_all_models()
+
+        for matcher, loader in ModelBase._hparams_loaders:
+            if matcher(dir_model):
+                return loader(dir_model)
+        return None
+
+    @classmethod
+    def register_hparams_loader(cls, matcher: HparamsMatcher) -> Callable[[HparamsLoader], HparamsLoader]:
+        def inner(loader: HparamsLoader) -> HparamsLoader:
+            cls._hparams_loaders.append((matcher, loader))
+            return loader
+        return inner
+
     @staticmethod
     def load_hparams(dir_model: Path, is_mistral_format: bool):
         if is_mistral_format:
@@ -1053,6 +1077,10 @@ class ModelBase:
             config = AutoConfig.from_pretrained(dir_model, trust_remote_code=False).to_dict()
         except Exception as e:
             logger.warning(f"Failed to load model config from {dir_model}: {e}")
+            if not (dir_model / "config.json").is_file():
+                config = ModelBase.load_hparams_guess(dir_model)
+                if config is not None:
+                    return config
             logger.warning("Trying to load config.json instead")
             with open(dir_model / "config.json", "r", encoding="utf-8") as f:
                 config = json.load(f)

conversion/chatglm.py

@@ -81,7 +81,7 @@ class ChatGLMModel(TextModel):
 
     @staticmethod
     def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
+        from transformers.convert_slow_tokenizer import bytes_to_unicode
         byte_encoder = bytes_to_unicode()
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 

conversion/deepseek.py

@@ -17,8 +17,11 @@ from .base import LazyTorchTensor, MmprojModel, ModelBase, TextModel, gguf, logg
 from .qwen import QwenModel
 
 
-@ModelBase.register("DeepseekOCRForCausalLM", "UnlimitedOCRForCausalLM")
+@ModelBase.register("DeepseekOCRForCausalLM")
 class DeepseekOCRVisionModel(MmprojModel):
+    # HF dynamic_preprocess() max_num, which differs per model
+    preproc_max_tiles = 9
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR
@@ -43,6 +46,9 @@ class DeepseekOCRVisionModel(MmprojModel):
         # @bluebread: there's no window_size in config but just add it here anyway
         self.gguf_writer.add_vision_window_size(self.hparams.get("window_size", 14))
 
+        self.gguf_writer.add_vision_preproc_min_tiles(2)
+        self.gguf_writer.add_vision_preproc_max_tiles(self.preproc_max_tiles)
+
         # SAM configuration
         sam_hparams = hparams['sam']
         self.gguf_writer.add_vision_sam_layers_count(sam_hparams['layers'])
@@ -93,8 +99,15 @@ class DeepseekOCRVisionModel(MmprojModel):
         return super().filter_tensors((name, gen))
 
 
+@ModelBase.register("UnlimitedOCRForCausalLM")
+class UnlimitedOCRVisionModel(DeepseekOCRVisionModel):
+    preproc_max_tiles = 32
+
+
 @ModelBase.register("DeepseekOCR2ForCausalLM")
 class DeepseekOCR2VisionModel(DeepseekOCRVisionModel):
+    preproc_max_tiles = 6
+
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.clip_projector_type = gguf.VisionProjectorType.DEEPSEEKOCR2
@@ -447,12 +460,43 @@ class DeepseekV2Model(TextModel):
 class DeepseekV32Model(DeepseekV2Model):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK32
     skip_mtp = False
+    supports_mtp_export = True
+    _n_main_layers: int | None = None
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-        self.block_count = self.hparams["num_hidden_layers"] + self.hparams.get("num_nextn_predict_layers", 0)
+        self.block_count = self.hparams["num_hidden_layers"]
+        if not self.no_mtp:
+            self.block_count += self.hparams.get("num_nextn_predict_layers", 0)
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
 
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        type(self)._n_main_layers = self.hparams["num_hidden_layers"]
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+
+        # DeepSeek V3.2 appends the NextN/MTP block past num_hidden_layers
+        # (model.layers.61 -> blk.61 in the 62-block file).
+        assert cls._n_main_layers is not None
+        is_mtp = (m := re.match(r"model\.layers\.(\d+)\.", name)) is not None and int(m.group(1)) >= cls._n_main_layers
+
+        # --no-mtp: drop the appended NextN block entirely.
+        if is_mtp and cls.no_mtp:
+            return None
+        # --mtp: keep ONLY NextN-block tensors plus the shared embeddings/
+        # norm/lm_head (so the resulting GGUF carries just the draft head).
+        if cls.mtp_only and not is_mtp and name not in (
+            "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+        ):
+            return None
+
+        return name, gen
+
     def set_vocab(self):
         from transformers import AutoTokenizer
         tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
@@ -463,7 +507,7 @@ class DeepseekV32Model(DeepseekV2Model):
         super().set_gguf_parameters()
 
         # NextN/MTP prediction layers
-        if (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
+        if not self.no_mtp and (num_nextn_predict_layers := self.hparams.get("num_nextn_predict_layers")) is not None:
             self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
 
         # DSA indexer parameters
@@ -475,7 +519,10 @@ class DeepseekV32Model(DeepseekV2Model):
 @ModelBase.register("DeepseekV4ForCausalLM")
 class DeepseekV4Model(TextModel):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK4
+    supports_mtp_export = True
     _skipped_mtp_tensors = 0
+    _dsv4_main_layers: int | None = None
+    _dsv4_nextn_layers: int = 0
 
     def __init__(self, *args, **kwargs):
         type(self)._skipped_mtp_tensors = 0
@@ -486,7 +533,16 @@ class DeepseekV4Model(TextModel):
         for key, value in raw_hparams.items():
             self.hparams.setdefault(key, value)
 
+        # workaround for special rope_parameters (main/compress) in transformers 5.x
+        if self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_type") is None:
+            if (rope_scaling := raw_hparams.get("rope_scaling")) is not None:
+                if "rope_type" not in rope_scaling and (rope_type := rope_scaling.get("type")) is not None:
+                    rope_scaling["rope_type"] = rope_type
+                self.rope_parameters.update(**rope_scaling)
+
         self.block_count = self.hparams["num_hidden_layers"]
+        if self.mtp_only:
+            self.block_count += self.hparams.get("num_nextn_predict_layers", 0)
         self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
 
         self._dsv4_fp8_dequantized: set[str] = set()
@@ -499,18 +555,71 @@ class DeepseekV4Model(TextModel):
             logger.info("Skipping %d DeepSeek-V4 MTP tensor(s) for conversion v0", type(self)._skipped_mtp_tensors)
 
         # add a default chat template; if the model has a built-in template, it will be overridden later
-        template_path = Path(__file__).parent.parent / "models" / "templates" / "deepseek-ai-DeepSeek-V4.jinja"
+        model_id_hint = self.remote_hf_model_id or self.dir_model.name
+        is_0731 = "0731" in model_id_hint
+        template_name = "deepseek-ai-DeepSeek-V4-Flash-0731.jinja" if is_0731 else "deepseek-ai-DeepSeek-V4.jinja"
+        template_path = Path(__file__).parent.parent / "models" / "templates" / template_name
         if template_path.is_file():
             with open(template_path, "r", encoding="utf-8") as f:
                 self.gguf_writer.add_chat_template(f.read())
 
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        type(self)._dsv4_main_layers = self.hparams["num_hidden_layers"]
+        type(self)._dsv4_nextn_layers = self.hparams.get("num_nextn_predict_layers", 0)
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
     @classmethod
     def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        name, _ = item
+        name, gen = item
         if name.startswith("mtp."):
-            cls._skipped_mtp_tensors += 1
-            return None
-        return super().filter_tensors(item)
+            if not cls.mtp_only:
+                cls._skipped_mtp_tensors += 1
+                return None
+
+            assert cls._dsv4_main_layers is not None
+            parts = name.split(".", 2)
+            if len(parts) < 3 or not parts[1].isdecimal():
+                raise ValueError(f"Unexpected DeepSeek-V4 MTP tensor {name!r}")
+
+            mtp_idx = int(parts[1])
+            if mtp_idx >= cls._dsv4_nextn_layers:
+                raise ValueError(f"Unexpected DeepSeek-V4 MTP layer {mtp_idx}")
+
+            bid = cls._dsv4_main_layers + mtp_idx
+            suffix = parts[2]
+            root_hc_head = {
+                "hc_head_fn",
+                "hc_head_base",
+                "hc_head_scale",
+            }
+            if suffix in root_hc_head:
+                name = suffix
+            elif suffix in (
+                "e_proj.weight", "e_proj.scale",
+                "h_proj.weight", "h_proj.scale",
+            ):
+                name = f"layers.{bid}.nextn.{suffix}"
+            elif suffix == "enorm.weight":
+                name = f"layers.{bid}.nextn.enorm.weight"
+            elif suffix == "hnorm.weight":
+                name = f"layers.{bid}.nextn.hnorm.weight"
+            elif suffix == "norm.weight":
+                name = f"layers.{bid}.nextn.shared_head_norm.weight"
+            else:
+                name = f"layers.{bid}.{suffix}"
+            return name, gen
+
+        if cls.mtp_only:
+            keep = name in (
+                "embed.weight",
+                "norm.weight",
+                "head.weight",
+                "head.scale",
+            )
+            if not keep:
+                return None
+
+        return super().filter_tensors((name, gen))
 
     @staticmethod
     def _float8_dtypes() -> tuple[torch.dtype, ...]:
@@ -565,6 +674,10 @@ class DeepseekV4Model(TextModel):
         self.gguf_writer.add_hyper_connection_sinkhorn_iterations(hparams["hc_sinkhorn_iters"])
         self.gguf_writer.add_hyper_connection_epsilon(hparams["hc_eps"])
         self.gguf_writer.add_hash_layer_count(hparams["num_hash_layers"])
+        if self.model_arch == gguf.MODEL_ARCH.DEEPSEEK4:
+            self.gguf_writer.add_embedding_length_out(hparams["hidden_size"] * hparams["hc_mult"])
+        if self.mtp_only and (num_nextn_predict_layers := hparams.get("num_nextn_predict_layers", 0)) > 0:
+            self.gguf_writer.add_nextn_predict_layers(num_nextn_predict_layers)
 
     def dequant_model(self):
         fp8_dtypes = self._float8_dtypes()
@@ -669,12 +782,37 @@ class DeepseekV4Model(TextModel):
         if self._dsv4_mxfp4_generated:
             return ()
 
-        consumed: list[str] = self._write_hash_routing_tensors()
+        consumed: list[str] = []
+        main_layers = self.hparams["num_hidden_layers"]
+        if not self.mtp_only:
+            consumed.extend(self._write_hash_routing_tensors())
+        elif self.hparams["num_hash_layers"] > 0:
+            for bid in range(self.hparams["num_hash_layers"]):
+                name = f"layers.{bid}.ffn.gate.tid2eid"
+                if name in self.model_tensors:
+                    consumed.extend(self._write_hash_routing_tensors())
+                    break
+
         for bid in range(self.block_count):
+            if self.mtp_only and bid < main_layers:
+                continue
             consumed.extend(self._write_mxfp4_expert_tensor(bid, "w1", gguf.MODEL_TENSOR.FFN_GATE_EXP))
             consumed.extend(self._write_mxfp4_expert_tensor(bid, "w2", gguf.MODEL_TENSOR.FFN_DOWN_EXP))
             consumed.extend(self._write_mxfp4_expert_tensor(bid, "w3", gguf.MODEL_TENSOR.FFN_UP_EXP))
 
+        for bid in range(main_layers, self.block_count):
+            e_name = f"layers.{bid}.nextn.e_proj.weight"
+            h_name = f"layers.{bid}.nextn.h_proj.weight"
+            if e_name not in self.model_tensors and h_name not in self.model_tensors:
+                continue
+            if e_name not in self.model_tensors or h_name not in self.model_tensors:
+                raise KeyError(f"Missing DeepSeek-V4 MTP e/h projection pair for block {bid}")
+
+            e_proj = LazyTorchTensor.to_eager(self.model_tensors[e_name]())
+            h_proj = LazyTorchTensor.to_eager(self.model_tensors[h_name]())
+            yield (f"layers.{bid}.nextn.eh_proj.weight", torch.cat((e_proj, h_proj), dim=1).contiguous())
+            consumed.extend((e_name, h_name))
+
         for name in consumed:
             del self.model_tensors[name]
 
@@ -737,6 +875,12 @@ class DeepseekV4Model(TextModel):
             "ffn.shared_experts.w1.weight": (gguf.MODEL_TENSOR.FFN_GATE_SHEXP, ".weight"),
             "ffn.shared_experts.w2.weight": (gguf.MODEL_TENSOR.FFN_DOWN_SHEXP, ".weight"),
             "ffn.shared_experts.w3.weight": (gguf.MODEL_TENSOR.FFN_UP_SHEXP, ".weight"),
+            "nextn.eh_proj.weight": (gguf.MODEL_TENSOR.NEXTN_EH_PROJ, ".weight"),
+            "nextn.enorm.weight": (gguf.MODEL_TENSOR.NEXTN_ENORM, ".weight"),
+            "nextn.hnorm.weight": (gguf.MODEL_TENSOR.NEXTN_HNORM, ".weight"),
+            "nextn.shared_head_norm.weight": (gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM, ".weight"),
+            "nextn.embed_tokens.weight": (gguf.MODEL_TENSOR.NEXTN_EMBED_TOKENS, ".weight"),
+            "nextn.shared_head_head.weight": (gguf.MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD, ".weight"),
         }
 
         tensor_name = match.group(2)
@@ -759,10 +903,12 @@ class DeepseekV4Model(TextModel):
         return [(self._format_dsv4_tensor_name(tensor_key, bid, suffix), data_torch)]
 
     def tensor_force_quant(self, name: str, new_name: str, bid: int | None, n_dims: int) -> gguf.GGMLQuantizationType | bool:
-        del new_name, bid  # unused
+        del bid  # unused
 
         if name in self._dsv4_fp8_dequantized and n_dims >= 2:
             return gguf.GGMLQuantizationType.Q8_0
+        if new_name.endswith(".nextn.eh_proj.weight"):
+            return gguf.GGMLQuantizationType.Q8_0
         if name in self._dsv4_f32_tensors:
             return gguf.GGMLQuantizationType.F32
         if name in self._dsv4_bf16_tensors and n_dims >= 2:
@@ -770,7 +916,122 @@ class DeepseekV4Model(TextModel):
 
         return False
 
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
     def prepare_tensors(self):
         super().prepare_tensors()
         self._is_mxfp4 = True
         self.ftype = gguf.LlamaFileType.MOSTLY_MXFP4_MOE
+
+
+@ModelBase.register("DeepseekV4DSparkModel")
+class DeepseekV4DSparkModel(DeepseekV4Model):
+    model_arch = gguf.MODEL_ARCH.DFLASH
+
+    _DSPARK_ROOT_MAP: dict[str, tuple[gguf.MODEL_TENSOR, str]] = {
+        "main_proj.weight": (gguf.MODEL_TENSOR.FC, ".weight"),
+        "main_norm.weight": (gguf.MODEL_TENSOR.ENC_OUTPUT_NORM, ".weight"),
+        "markov_head.markov_w1.weight": (gguf.MODEL_TENSOR.DSPARK_MARKOV_W1, ".weight"),
+        "markov_head.markov_w2.weight": (gguf.MODEL_TENSOR.DSPARK_MARKOV_W2, ".weight"),
+        "confidence_head.proj.weight": (gguf.MODEL_TENSOR.DSPARK_CONF_PROJ, ".weight"),
+    }
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        self.block_count = 1 + max(
+            int(match.group(1)) for name in self.model_tensors
+            if (match := re.match(r"layers\.(\d+)\.", name))
+        )
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+        self.hparams["compress_ratios"] = [0] * self.block_count
+        self.hparams["num_hash_layers"] = 0
+
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        if remote_hf_model_id is None:
+            return super().index_tensors()
+
+        with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f:
+            weight_map = json.load(f)["weight_map"]
+
+        part_names = sorted({
+            part_name for name, part_name in weight_map.items()
+            if name.startswith("mtp.")
+        })
+        tensors: dict[str, Callable[[], Tensor]] = {}
+
+        for part_name in part_names:
+            from huggingface_hub import hf_hub_download
+
+            logger.info("gguf: caching remote DSpark part '%s'", part_name)
+            part_path = Path(hf_hub_download(repo_id=remote_hf_model_id, filename=part_name))
+            with gguf.utility.SafetensorsLocal(part_path) as model_part:
+                for name in model_part:
+                    data = model_part[name]
+                    data_gen = lambda data=data: LazyTorchTensor.from_local_tensor(data)  # noqa: E731
+                    if titem := self.filter_tensors((name, data_gen)):
+                        tensor_name, tensor_gen = titem
+                        tensors[tensor_name] = tensor_gen
+
+        return tensors
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if not name.startswith("mtp."):
+            return None
+        return super().filter_tensors((cls._rekey_mtp_tensor_name(name), gen))
+
+    @staticmethod
+    def _rekey_mtp_tensor_name(name: str) -> str:
+        match = re.match(r"mtp\.(\d+)\.(.+)$", name)
+        if match is None:
+            raise ValueError(f"Unexpected DSpark tensor {name!r}")
+
+        stage, rest = match.group(1), match.group(2)
+        root_names = (
+            "main_proj.scale",
+            "norm.weight",
+            "hc_head_fn",
+            "hc_head_base",
+            "hc_head_scale",
+        )
+        if rest in DeepseekV4DSparkModel._DSPARK_ROOT_MAP or rest in root_names:
+            return rest
+        return f"layers.{stage}.{rest}"
+
+    def _map_dsv4_tensor_name(self, name: str, bid: int | None) -> tuple[gguf.MODEL_TENSOR, str]:
+        if name in self._DSPARK_ROOT_MAP:
+            return self._DSPARK_ROOT_MAP[name]
+        return super()._map_dsv4_tensor_name(name, bid)
+
+    def set_vocab(self):
+        if self.target_model_dir is None:
+            raise ValueError("DeepSeek-V4 DSpark requires --target-model-dir with the target tokenizer")
+
+        original_dir = self.dir_model
+        try:
+            self.dir_model = self.target_model_dir
+            super().set_vocab()
+        finally:
+            self.dir_model = original_dir
+
+        self.gguf_writer.add_mask_token_id(self.hparams["dspark_noise_token_id"])
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        self.gguf_writer.add_block_size(self.hparams["dspark_block_size"])
+        self.gguf_writer.add_target_layers([layer + 1 for layer in self.hparams["dspark_target_layer_ids"]])

conversion/exaone.py

@@ -123,7 +123,9 @@ class Exaone4Model(TextModel):
                 yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
 
 
-@ModelBase.register("ExaoneMoEForCausalLM")
+# note: transformers >= 5.1 renamed the class to "ExaoneMoeForCausalLM" (lowercase 'e'),
+#       so accept both spellings - LG AI have updated the configs of already-released models
+@ModelBase.register("ExaoneMoEForCausalLM", "ExaoneMoeForCausalLM")
 class ExaoneMoEModel(Exaone4Model):
     model_arch = gguf.MODEL_ARCH.EXAONE_MOE
 

conversion/gemma.py

@@ -665,7 +665,18 @@ class Gemma4Model(Gemma3Model):
         swa_layers = [t == "sliding_attention" for t in self.hparams["layer_types"]]
         self.gguf_writer.add_sliding_window_pattern(swa_layers)
 
-        head_dim_full = self.hparams["global_head_dim"]
+        per_layer_config = self.hparams.get("per_layer_config")
+        layer_types = self.hparams.get("layer_types", [])
+        if (head_dim_full := self.hparams.get("global_head_dim")) is None and per_layer_config is not None:
+            for layer_idx, layer_config in per_layer_config.items():
+                layer_idx = int(layer_idx)
+                if layer_idx < len(layer_types):
+                    if layer_types[layer_idx] == "full_attention" and "head_dim" in layer_config:
+                        head_dim_full = layer_config["head_dim"]
+                        break
+
+        assert head_dim_full is not None
+
         head_dim_swa = self.hparams["head_dim"]
         # correct the head dim for global/swa layers
         self.gguf_writer.add_key_length(head_dim_full)
@@ -685,8 +696,14 @@ class Gemma4Model(Gemma3Model):
             n_ff_arr = [n_ff if il < first_kv_shared_layer_idx else n_ff * 2 for il in range(self.block_count)]
             self.gguf_writer.add_feed_forward_length(n_ff_arr)
 
-        # handle num_global_key_value_heads
-        num_key_value_heads_full = self.hparams.get("num_global_key_value_heads")
+        if (num_key_value_heads_full := self.hparams.get("num_global_key_value_heads")) is None and per_layer_config is not None:
+            for layer_idx, layer_config in per_layer_config.items():
+                layer_idx = int(layer_idx)
+                if layer_idx < len(layer_types):
+                    if layer_types[layer_idx] == "full_attention" and "num_key_value_heads" in layer_config:
+                        num_key_value_heads_full = layer_config["num_key_value_heads"]
+                        break
+
         num_key_value_heads_swa = self.hparams.get("num_key_value_heads")
         if num_key_value_heads_full is not None and num_key_value_heads_swa is not None:
             value_arr = [num_key_value_heads_swa if is_swa else num_key_value_heads_full for is_swa in swa_layers]
@@ -708,7 +725,19 @@ class Gemma4Model(Gemma3Model):
         # IMPORTANT: this ROPE_FREQS tensor is ONLY used by the full_attention layers
         rope_params_full = self.hparams["rope_parameters"]["full_attention"]
         assert rope_params_full["rope_type"] == "proportional"
-        head_dim_full = (self.hparams["global_head_dim"])
+
+        per_layer_config = self.hparams.get("per_layer_config")
+        if (head_dim_full := self.hparams.get("global_head_dim")) is None and per_layer_config is not None:
+            layer_types = self.hparams.get("layer_types", [])
+            for layer_idx, layer_config in per_layer_config.items():
+                layer_idx = int(layer_idx)
+                if layer_idx < len(layer_types):
+                    if layer_types[layer_idx] == "full_attention" and "head_dim" in layer_config:
+                        head_dim_full = layer_config["head_dim"]
+                        break
+
+        assert head_dim_full is not None
+
         partial_rotary_factor_full = rope_params_full["partial_rotary_factor"]
         n_rot_full = int(head_dim_full * partial_rotary_factor_full / 2)
         n_unrot_full = int(head_dim_full / 2) - n_rot_full

conversion/glm.py

@@ -206,10 +206,70 @@ class Glm4MoeModel(TextModel):
 @ModelBase.register("Glm4MoeLiteForCausalLM")
 class Glm4MoeLiteModel(DeepseekV2Model):
     model_arch = gguf.MODEL_ARCH.DEEPSEEK2
+    skip_mtp = False
+    supports_mtp_export = True
+    _n_main_layers: int | None = None
 
     def set_vocab(self):
         return self._set_vocab_glm()
 
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        num_hidden_layers = self.hparams["num_hidden_layers"]
+        self.num_nextn_predict_layers = self.hparams.get("num_nextn_predict_layers", 0)
+        self.skip_mtp = self.no_mtp or self.num_nextn_predict_layers == 0
+
+        if self.skip_mtp:
+            self.block_count = num_hidden_layers
+        else:
+            self.block_count = num_hidden_layers + self.num_nextn_predict_layers
+
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        if self.skip_mtp:
+            return
+
+        self.gguf_writer.add_nextn_predict_layers(self.num_nextn_predict_layers)
+
+    def index_tensors(self, remote_hf_model_id: str | None = None):
+        type(self)._n_main_layers = self.hparams["num_hidden_layers"]
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)
+
+    @classmethod
+    def filter_tensors(cls, item):
+        if (titem := super().filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+
+        if cls._n_main_layers is not None:
+            match = re.match(r"model\.layers\.(\d+)\.", name)
+            is_mtp = match is not None and int(match.group(1)) >= cls._n_main_layers
+            if is_mtp and cls.no_mtp:
+                return None
+            if cls.mtp_only and not is_mtp and name not in (
+                "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+            ):
+                return None
+
+        return name, gen
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
 
 @ModelBase.register("GlmMoeDsaForCausalLM")
 class GlmMoeDsaModel(DeepseekV2Model):

conversion/granite.py

@@ -123,6 +123,166 @@ class GraniteMoeModel(GraniteModel):
         yield from super().modify_tensors(data_torch, name, bid)
 
 
+@ModelBase.register("GraniteSwitchForCausalLM")
+class GraniteSwitchModel(GraniteMoeModel):
+    """Dense, all-attention Granite with N per-token embedded LoRA adapters, stacked
+    over the adapter dim with a zero adapter at slot 0 (N = num_adapters + 1)."""
+    model_arch = gguf.MODEL_ARCH.GRANITE_SWITCH
+
+    # permute q/k per-slice below (NORM-rope layout), not via the parent's auto-permute
+    undo_permute = False
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        # the weightless switch reserves one cache slot: one fewer block than num_hidden_layers
+        self.block_count = self.block_count - 1
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+        self._n_adapters = int(self.hparams["num_adapters"])
+        self._max_lora_rank = int(self.hparams["max_lora_rank"])
+        self._n_slots = self._n_adapters + 1  # +1 for the zero slot at index 0
+
+        n_head = int(self.hparams["num_attention_heads"])
+        n_kv_head = int(self.hparams["num_key_value_heads"])
+        head_dim = (
+            self.hparams.get("projection_head_dim")
+            or self.hparams.get("head_dim")
+            or (self.hparams["hidden_size"] // n_head)
+        )
+        self._n_head = n_head
+        self._n_kv_head = n_kv_head
+        self._head_dim = int(head_dim)
+        self._q_size = n_head * self._head_dim
+        self._kv_size = n_kv_head * self._head_dim
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # dense: pin expert_used_count to 0 (config carries a leftover num_experts_per_tok)
+        if not self.hparams.get("num_local_experts"):
+            self.gguf_writer.add_expert_used_count(0)
+
+        self.gguf_writer.add_adapter_count(self._n_adapters)
+        self.gguf_writer.add_adapter_lora_rank(self._max_lora_rank)
+        self.gguf_writer.add_adapter_token_ids_activate(self.hparams["adapter_token_ids"])
+        self.gguf_writer.add_adapter_token_ids_substitute(self.hparams["adapter_substitute_token_ids"])
+        router_gain = float(self.hparams.get("control_token_gain", 15.0))
+        self.gguf_writer.add_adapter_router_gain(router_gain)
+        logger.info("gguf: (graniteswitch) num_adapters=%s max_lora_rank=%s n_slots=%s router_gain=%s", self._n_adapters, self._max_lora_rank, self._n_slots, router_gain)
+
+    def _lora_a(self, data: Tensor) -> Tensor:
+        # on-disk A: [n_adapters, 1, max_rank, in] -> [n_adapters+1, max_rank, in]
+        a = data.squeeze(1)
+        zero = torch.zeros_like(a[:1])
+        return torch.cat([zero, a], dim=0).contiguous()
+
+    def _lora_b(self, data: Tensor, permute_n_head: int | None = None) -> Tensor:
+        # on-disk B: [n_adapters, 1, out, max_rank] -> [n_adapters+1, out, max_rank]
+        b = data.squeeze(1)
+        if permute_n_head is not None:
+            # permute each adapter's B output rows to match the permuted q/k base
+            b = torch.stack([self.permute(b[i], permute_n_head, permute_n_head) for i in range(b.shape[0])], dim=0)
+        zero = torch.zeros_like(b[:1])
+        return torch.cat([zero, b], dim=0).contiguous()
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        T = gguf.MODEL_TENSOR
+
+        # skip the weightless switch + control-token buffers (rebuilt at load time)
+        bare = name.split(".")[-1]
+        if (
+            name.startswith("model.switch.") or name.startswith("switch.")
+            or bare in ("adapter_token_ids", "control_to_substitute_lut")
+        ):
+            return
+
+        if "self_attn.qkv_proj" in name:
+            if name.endswith("base_layer.weight"):
+                # fused [q|k|v] rows: permute q/k row-blocks for ggml's NORM-rope layout
+                q, k, v = data_torch.split([self._q_size, self._kv_size, self._kv_size], dim=0)
+                q = self.permute(q, self._n_head, self._n_head)
+                k = self.permute(k, self._n_kv_head, self._n_kv_head)
+                fused = torch.cat([q, k, v], dim=0)
+                yield (self.format_tensor_name(T.ATTN_QKV, bid), fused)
+                return
+            if "lora_A_slices." in name:
+                slot = int(name.rsplit(".", 1)[1])
+                key = {0: T.ATTN_Q, 1: T.ATTN_K, 2: T.ATTN_V}[slot]
+                yield (self.format_tensor_name(key, bid, suffix=".lora_a"), self._lora_a(data_torch))
+                return
+            if "lora_B_slices." in name:
+                slot = int(name.rsplit(".", 1)[1])
+                key, ph = {
+                    0: (T.ATTN_Q, self._n_head),
+                    1: (T.ATTN_K, self._n_kv_head),
+                    2: (T.ATTN_V, None),
+                }[slot]
+                yield (self.format_tensor_name(key, bid, suffix=".lora_b"), self._lora_b(data_torch, ph))
+                return
+            raise ValueError(f"Unexpected qkv_proj tensor: {name}")
+
+        if "self_attn.o_proj" in name:
+            if name.endswith("base_layer.weight"):
+                yield (self.format_tensor_name(T.ATTN_OUT, bid), data_torch)
+                return
+            if name.endswith("lora_A"):
+                yield (self.format_tensor_name(T.ATTN_OUT, bid, suffix=".lora_a"), self._lora_a(data_torch))
+                return
+            if name.endswith("lora_B"):
+                yield (self.format_tensor_name(T.ATTN_OUT, bid, suffix=".lora_b"), self._lora_b(data_torch))
+                return
+            raise ValueError(f"Unexpected o_proj tensor: {name}")
+
+        if "shared_mlp.input_linear" in name:
+            ffn = self.hparams["shared_intermediate_size"]
+            if name.endswith("base_layer.weight"):
+                gate, up = data_torch.split([ffn, ffn], dim=0)
+                yield (self.format_tensor_name(T.FFN_GATE, bid), gate)
+                yield (self.format_tensor_name(T.FFN_UP, bid), up)
+                return
+            if "lora_A_slices." in name:
+                slot = int(name.rsplit(".", 1)[1])
+                key = {0: T.FFN_GATE, 1: T.FFN_UP}[slot]
+                yield (self.format_tensor_name(key, bid, suffix=".lora_a"), self._lora_a(data_torch))
+                return
+            if "lora_B_slices." in name:
+                slot = int(name.rsplit(".", 1)[1])
+                key = {0: T.FFN_GATE, 1: T.FFN_UP}[slot]
+                yield (self.format_tensor_name(key, bid, suffix=".lora_b"), self._lora_b(data_torch))
+                return
+            raise ValueError(f"Unexpected shared_mlp.input_linear tensor: {name}")
+
+        if "shared_mlp.output_linear" in name:
+            if name.endswith("base_layer.weight"):
+                yield (self.format_tensor_name(T.FFN_DOWN, bid), data_torch)
+                return
+            if name.endswith("lora_A"):
+                yield (self.format_tensor_name(T.FFN_DOWN, bid, suffix=".lora_a"), self._lora_a(data_torch))
+                return
+            if name.endswith("lora_B"):
+                yield (self.format_tensor_name(T.FFN_DOWN, bid, suffix=".lora_b"), self._lora_b(data_torch))
+                return
+            raise ValueError(f"Unexpected shared_mlp.output_linear tensor: {name}")
+
+        if bid is not None and ".layers." in name and (
+            "input_layernorm" in name or "post_attention_layernorm" in name
+        ):
+            key = T.ATTN_NORM if "input_layernorm" in name else T.FFN_NORM
+            yield (self.format_tensor_name(key, bid), data_torch)
+            return
+
+        if name in ("model.embed_tokens.weight", "embed_tokens.weight"):
+            yield (self.format_tensor_name(T.TOKEN_EMBD), data_torch)
+            return
+        if name in ("model.norm.weight", "norm.weight"):
+            yield (self.format_tensor_name(T.OUTPUT_NORM), data_torch)
+            return
+        if name == "lm_head.weight":
+            return  # tied to token_embd
+
+        raise ValueError(f"graniteswitch: unhandled tensor {name!r} (bid={bid})")
+
+
 @ModelBase.register("GraniteMoeHybridForCausalLM", "BambaForCausalLM")
 class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
     """GraniteHybrid is a hybrid SSM + Attention model that uses Mamba2 SSM

conversion/llama.py

@@ -119,7 +119,7 @@ class LlamaModel(TextModel):
         path_tekken_json = self.dir_model / "tekken.json"
         path_tokenizer_json = self.dir_model / "tokenizer.json"
         if path_tekken_json.is_file() and not path_tokenizer_json.is_file():
-            self._set_vocab_mistral()
+            return self._set_vocab_mistral()
 
         tokenizer_config_file = self.dir_model / 'tokenizer_config.json'
         if tokenizer_config_file.is_file():

conversion/minicpm.py

@@ -137,6 +137,15 @@ class MiniCPMV4_6TextModel(Qwen3_5TextModel):
 class MiniCPMV4_6VisionModel(MmprojModel):
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        self.downsample_mode = self.preprocessor_config.get("downsample_mode", "16x")
+        if self.downsample_mode not in {"4x", "16x"}:
+            raise ValueError(f"Unsupported downsample mode: {self.downsample_mode}")
+        if self.downsample_mode == "4x":
+            self.model_tensors = {
+                name: tensor for name, tensor in self.model_tensors.items()
+                if ".vit_merger." not in name
+            }
+
         if self.hparams_vision is not None:
             # In MiniCPM-V 4.6 `vision_config.image_size` (980) describes the SigLIP
             # positional embedding bucket grid (70 x 70), while the per-slice processing
@@ -156,8 +165,8 @@ class MiniCPMV4_6VisionModel(MmprojModel):
         # (mapped to PROJECTOR_TYPE_MINICPMV4_6).
         self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MINICPMV4_6)
 
-        # ViT merger 2x2 + final merger 2x2 = 4x spatial merge per dimension; used for slice alignment
-        self.gguf_writer.add_vision_projector_scale_factor(4)
+        self.gguf_writer.add_vision_projector_scale_factor(
+            2 if self.downsample_mode == "4x" else 4)
 
         # borrow wa_layer_indexes for vit_merger insertion point
         insert_layer_id = int(self.global_config.get(

conversion/minimax.py

@@ -1,13 +1,121 @@
 from __future__ import annotations
 
-from typing import TYPE_CHECKING
+from typing import Iterable, Sequence, TYPE_CHECKING
 
 import torch
 
 if TYPE_CHECKING:
     from torch import Tensor
 
-from .base import ModelBase, TextModel, MmprojModel, gguf
+from .base import ModelBase, TextModel, MmprojModel, gguf, logger
+
+
+@ModelBase.register("MiniMaxText01ForCausalLM")
+@ModelBase.register("MiniMaxM1ForCausalLM")
+class MiniMaxText01Model(TextModel):
+    model_arch = gguf.MODEL_ARCH.MINIMAX01
+
+    def _get_suppress_tokens(self) -> Sequence[int] | None:
+        import json
+        from transformers import AutoTokenizer
+        from .base import LazyTorchTensor
+
+        # check added tokens embeddings in embeddings tensor for zero-valued embeddings
+        # they get in the way of the token sampling process and must be suppressed
+
+        tokenizer = AutoTokenizer.from_pretrained(self.dir_model, trust_remote_code=True)
+        tokenizer_vocab_size = tokenizer.vocab_size
+
+        with open(self.dir_model / "model.safetensors.index.json", "r", encoding="utf-8") as f:
+            weight_map = json.load(f)["weight_map"]
+
+        embeddings_tensor_name = "model.embed_tokens.weight"
+        embeddings_shard_name = weight_map[embeddings_tensor_name]
+        with gguf.utility.SafetensorsLocal(self.dir_model / embeddings_shard_name) as model_shard:
+            embeddings_data = model_shard[embeddings_tensor_name]
+
+        embeddings_weights_dtype = LazyTorchTensor._dtype_str_map[embeddings_data.dtype]
+        embeddings_weights = torch.from_numpy(embeddings_data.mmap_bytes()).view(embeddings_weights_dtype).reshape(embeddings_data.shape)
+        embeddings_vocab_size = embeddings_weights.shape[0]
+
+        embeddings_added_tokens = embeddings_weights[tokenizer_vocab_size:embeddings_vocab_size]
+        embeddings_zero_rows = torch.all(embeddings_added_tokens == 0, dim=1)
+        tokens_zero_embeddings_ids = (torch.nonzero(embeddings_zero_rows, as_tuple=False).flatten() + tokenizer_vocab_size).tolist()
+
+        return tokens_zero_embeddings_ids
+
+    def set_vocab(self) -> None:
+        from pathlib import Path
+
+        self._set_vocab_gpt2()
+
+        for tmpl_file in [
+            self.dir_model / "chat_template.jinja",
+            Path(__file__).parent.parent / "models" / "templates" / "MiniMax-M1.jinja"
+        ]:
+            if tmpl_file.is_file():
+                self.gguf_writer.add_chat_template(tmpl_file.read_text(encoding="utf-8"))
+                logger.info(f"Chat template overridden with {tmpl_file}.")
+                break
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        suppress_tokens = self._get_suppress_tokens()
+        if suppress_tokens:
+            logger.info(f"Suppressing tokens with zero embeddings {suppress_tokens}")
+            self.gguf_writer.add_suppress_tokens(suppress_tokens)
+
+        layernorm_full_attention_alpha = self.hparams["layernorm_full_attention_alpha"]
+        layernorm_full_attention_beta = self.hparams["layernorm_full_attention_beta"]
+        layernorm_linear_attention_alpha = self.hparams["layernorm_linear_attention_alpha"]
+        layernorm_linear_attention_beta = self.hparams["layernorm_linear_attention_beta"]
+        layernorm_mlp_alpha = self.hparams["layernorm_mlp_alpha"]
+        layernorm_mlp_beta = self.hparams["layernorm_mlp_beta"]
+        assert layernorm_full_attention_alpha == layernorm_linear_attention_alpha == layernorm_mlp_alpha
+        assert layernorm_full_attention_beta == layernorm_linear_attention_beta == layernorm_mlp_beta == 1.0
+        # we do not store the layernorm betas as they are all 1.0
+        # layernorm alphas are stored as single residual_scale hparam
+        self.gguf_writer.add_residual_scale(layernorm_full_attention_alpha)
+
+        self.gguf_writer.add_rope_dimension_count(self.hparams["rotary_dim"])
+
+    _experts: list[dict[str, Tensor]] | None = None
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # process the experts separately
+        if name.find("block_sparse_moe.experts") != -1:
+            n_experts = self.hparams["num_local_experts"]
+
+            assert bid is not None
+
+            if self._experts is None:
+                self._experts = [{} for _ in range(self.block_count)]
+
+            self._experts[bid][name] = data_torch
+
+            if len(self._experts[bid]) >= n_experts * 3:
+                # merge the experts into a single 3d tensor
+                for wid in ["w1", "w2", "w3"]:
+                    datas: list[Tensor] = []
+
+                    for xid in range(n_experts):
+                        ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight"
+                        datas.append(self._experts[bid][ename])
+                        del self._experts[bid][ename]
+
+                    data_torch = torch.stack(datas, dim=0)
+
+                    merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight"
+
+                    new_name = self.map_tensor_name(merged_name)
+
+                    yield from super().modify_tensors(data_torch, new_name, bid)
+                return
+            else:
+                return
+
+        yield from super().modify_tensors(data_torch, name, bid)
 
 
 @ModelBase.register("MiniMaxM2ForCausalLM")

conversion/muse_glimmer.py

@@ -0,0 +1,179 @@
+from __future__ import annotations
+
+import json
+from typing import Any, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import MmprojModel, ModelBase, TextModel, gguf
+
+
+def _unpermute_for_rope(tensor: "Tensor", n_heads: int) -> "Tensor":
+    """Invert transformers' `_permute_for_rope`: HF stores Q/K in rotate_half layout,
+    llama.cpp consumes the interleaved (NORM) layout."""
+    if tensor.ndim == 2:
+        dim1, dim2 = tensor.shape
+        return tensor.view(n_heads, 2, dim1 // n_heads // 2, dim2).transpose(1, 2).reshape(dim1, dim2)
+    if tensor.ndim == 1:
+        (dim1,) = tensor.shape
+        return tensor.view(n_heads, 2, dim1 // n_heads // 2).transpose(1, 2).reshape(dim1)
+    raise ValueError(f"_unpermute_for_rope: unexpected shape {tuple(tensor.shape)}")
+
+
+@ModelBase.register("MuseGlimmerForConditionalGeneration")
+class MuseGlimmerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.MUSE_GLIMMER
+
+    def norm_shift(self, name: str) -> float:
+        # All four layer norms use 1, the final norm uses 0.
+        return 1.0 if name.endswith("layernorm.weight") else 0.0
+
+    def set_vocab(self):
+        self._set_vocab_gpt2()
+
+        from transformers import AutoTokenizer
+        tok = AutoTokenizer.from_pretrained(self.dir_model)
+        eot_id = tok.convert_tokens_to_ids("<|eot|>")
+        if isinstance(eot_id, int) and eot_id >= 0:
+            self.gguf_writer.add_eot_token_id(eot_id)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        hparams = self.hparams
+
+        self.gguf_writer.add_final_logit_softcapping(hparams["final_logit_softcapping"])
+        self.gguf_writer.add_logit_scale(hparams["output_multiplier"])
+        self.gguf_writer.add_sliding_window(hparams["sliding_window"])
+        self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in hparams["layer_types"]])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        shift = self.norm_shift(name)
+        if shift != 0.0:
+            data_torch = data_torch + shift
+
+        # Invert transformers' `_permute_for_rope` on Q/K, we keep ggml's NORM (interleaved) rope
+        if ".self_attn.q_proj." in name:
+            data_torch = _unpermute_for_rope(data_torch, int(self.hparams["num_attention_heads"]))
+        elif ".self_attn.k_proj." in name:
+            data_torch = _unpermute_for_rope(data_torch, int(self.hparams["num_key_value_heads"]))
+
+        # Synthesize QK-norm weights to absorb qk_scale_factor.
+        # MuseGlimmer implementation: scaleless RMSNorm followed by qk_scale_factor..
+        if bid is not None and name.endswith(f"model.layers.{bid}.self_attn.q_proj.weight"):
+            head_dim = self.hparams["head_dim"]
+            q_scale = float(self.hparams["qk_scale_factor"])
+            yield (
+                self.map_tensor_name(f"model.layers.{bid}.self_attn.q_norm.weight"),
+                torch.full((head_dim,), q_scale, dtype=torch.float32),
+            )
+            yield (
+                self.map_tensor_name(f"model.layers.{bid}.self_attn.k_norm.weight"),
+                torch.ones((head_dim,), dtype=torch.float32),
+            )
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("MuseGlimmerForConditionalGeneration")
+class MuseGlimmerVisionModel(MmprojModel):
+    def get_vision_config(self) -> dict[str, Any] | None:
+        c = self.global_config.get("vision_config")
+        if not c:
+            return None
+        # MuseGlimmer actually uses dynamic size, initialize with nominal size
+        image_size = c["pos_emb_height"] * c["patch_size"] * c["merge_size"]
+        return {**c, "image_size": image_size}
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        assert self.hparams_vision is not None
+        c = self.hparams_vision  # enriched vision_config from get_vision_config()
+
+        self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MUSE_GLIMMER)
+        self.gguf_writer.add_vision_attention_layernorm_eps(float(c["layer_norm_eps"]))
+        self.gguf_writer.add_vision_spatial_merge_size(int(c["merge_size"]))
+
+    @classmethod
+    def filter_tensors(cls, item):
+        name, gen = item
+        keep = ("model.vision_tower.", "model.vision_adapter.", "model.vision_projection.")
+        if not any(name.startswith(k) for k in keep):
+            return None
+        return super().filter_tensors((name, gen))
+
+    # 3-layer projector MLP
+    _MM_MLP_MAP = {
+        "model.vision_adapter.fc1": (gguf.MODEL_TENSOR.V_MMPROJ, 0),
+        "model.vision_adapter.fc2": (gguf.MODEL_TENSOR.V_MMPROJ, 1),
+        "model.vision_projection":  (gguf.MODEL_TENSOR.V_MMPROJ, 2),
+    }
+
+    def modify_tensors(self, data_torch, name, bid):
+        assert self.hparams_vision is not None
+        if ".attn.q_proj." in name or ".attn.k_proj." in name:
+            n_heads = int(self.hparams_vision["num_attention_heads"])
+            data_torch = _unpermute_for_rope(data_torch, n_heads)
+        # Lay out the pt=2 temporal slabs of the patch embedding as a conv2d for build_inp()
+        if name.endswith("patch_embedder.patch_embedding.weight"):
+            n_embd = data_torch.shape[0]
+            pt = int(self.hparams_vision["patch_temporal"])
+            ps = int(self.hparams_vision["patch_size"])
+            data_torch = data_torch.view(n_embd, pt, 3, ps, ps).sum(dim=1)  # (n_embd, 3, ps, ps)
+        stem, _, suffix = name.rpartition(".")
+        if stem in self._MM_MLP_MAP:
+            tensor_key, idx = self._MM_MLP_MAP[stem]
+            yield (self.format_tensor_name(tensor_key, bid=idx, suffix="." + suffix), data_torch)
+            return
+        yield (self.map_tensor_name(name), data_torch)
+
+
+@ModelBase.register("MuseGlimmerAssistantModel")
+class MuseGlimmerAssistantModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.DFLASH
+
+    def set_vocab(self):
+        if self.target_model_dir is None:
+            raise ValueError(
+                "MuseGlimmerAssistant (DFlash drafter) requires --target-model-dir pointing to the "
+                "target MuseGlimmer HF directory"
+            )
+
+        original_dir = self.dir_model
+        self.dir_model = self.target_model_dir
+
+        from . import get_model_class
+        with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f:
+            target_arch = json.load(f)["architectures"][0]
+        target_cls = get_model_class(target_arch)
+        if target_cls is not type(self):
+            target_cls.set_vocab(self)  # ty: ignore[unresolved-attribute]
+        else:
+            super().set_vocab()
+
+        self.dir_model = original_dir
+
+        mask_token_id = self.hparams.get("mask_token_id")
+        if mask_token_id is not None:
+            self.gguf_writer.add_mask_token_id(int(mask_token_id))
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+        h = self.hparams
+
+        self.gguf_writer.add_block_size(int(h["block_size"]))
+
+        # dflash.target_layers[k] refers to the inputs going into the ith layer, which come from the (i-1)th layer's output.
+        # The transformers configuration refers to the outputs being recorded.
+        self.gguf_writer.add_target_layers([int(x) + 1 for x in h["target_layer_ids"]])
+
+        if h.get("sliding_window") and h.get("layer_types"):
+            self.gguf_writer.add_sliding_window(int(h["sliding_window"]))
+            self.gguf_writer.add_sliding_window_pattern([t == "sliding_attention" for t in h["layer_types"]])
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # DFlash defaults to NEOX (rotate_half) rope, matching transformers HF layout for Q/K, QK-norms
+        # no permutation needed.
+        yield (self.map_tensor_name(name), data_torch)

conversion/nemotron.py

@@ -197,6 +197,7 @@ class NemotronHModel(GraniteHybridModel):
     """Hybrid mamba2/attention model from NVIDIA"""
     model_arch = gguf.MODEL_ARCH.NEMOTRON_H
     is_moe: bool = False
+    supports_mtp_export = True
 
     def __init__(self, *args, **kwargs):
         # We have to determine the correct model architecture (MoE vs non-MoE) before
@@ -236,6 +237,25 @@ class NemotronHModel(GraniteHybridModel):
             self._ssm_layers = [i for i, val in enumerate(pattern) if val == "mamba"]
             self._mlp_layers = [i for i, val in enumerate(pattern) if val == "moe"]
 
+        # `--no-mtp` drops it entirely; `--mtp` exports only the MTP head
+        self._mtp_bid: int | None = None
+        if self.is_moe and not self.no_mtp:
+            n_nextn = self.hparams.get("num_nextn_predict_layers", 0) or 0
+            if n_nextn > 0:
+                assert n_nextn == 1, (
+                    "NemotronH MTP conversion currently supports num_nextn_predict_layers == 1"
+                )
+                self._mtp_bid = self.block_count
+                self.block_count += 1
+                # The folded MTP block carries both an attention sub-layer and a
+                # MoE sub-layer, so register it as both so the per-layer metadata arrays cover it
+                self._attn_layers.append(self._mtp_bid)
+                self._mlp_layers.append(self._mtp_bid)
+                self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+        if self.mtp_only and self._mtp_bid is None:
+            raise ValueError("--mtp was requested, but this model does not contain a supported MTP head")
+
     def get_attn_layers(self):
         pattern = self.hparams.get("hybrid_override_pattern") or self.hparams.get("layers_block_type")
         if pattern is None:
@@ -246,6 +266,44 @@ class NemotronHModel(GraniteHybridModel):
 
         return [i for i, val in enumerate(pattern) if val == "attention"]
 
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+        if name.startswith("mtp."):
+            # --no-mtp: drop the MTP head entirely
+            if cls.no_mtp:
+                return None
+        elif cls.mtp_only:
+            # --mtp: export the MTP head plus the tensors it shares with the target model
+            # Include lm_head scale sidecars so NVFP4 packing sees them.
+            keep = name in (
+                "backbone.embeddings.weight",
+                "backbone.norm_f.weight",
+                "lm_head.weight",
+                "lm_head.weight_scale",
+                "lm_head.weight_scale_2",
+                "lm_head.weight_scale_inv",
+                "lm_head.input_scale",
+                "lm_head.input_global_scale",
+                "lm_head.weight_global_scale",
+                "lm_head.weight_packed",
+            )
+            if not keep:
+                return None
+        return super().filter_tensors((name, gen))
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)
+
+        if not self.mtp_only or not from_dir:
+            return
+        output_type: str = self.ftype.name.partition("_")[2]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
     def set_gguf_parameters(self):
         super().set_gguf_parameters()
 
@@ -284,6 +342,10 @@ class NemotronHModel(GraniteHybridModel):
             if (latent_size := self.hparams.get("moe_latent_size")) is not None:
                 self.gguf_writer.add_moe_latent_size(latent_size)
 
+        # MTP head: number of trailing NextN blocks
+        if self._mtp_bid is not None:
+            self.gguf_writer.add_nextn_predict_layers(self.hparams["num_nextn_predict_layers"])
+
     def set_vocab(self):
         # The NemotronH config uses pattern characters (e.g. '-') that may not
         # be supported by the installed transformers version. AutoTokenizer
@@ -350,15 +412,24 @@ class NemotronHModel(GraniteHybridModel):
         if not self.is_moe:
             self.gguf_writer.add_add_bos_token(True)
 
-    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-        if self.is_moe and bid is not None:
-            # Skip Multi-Token Prediction (MTP) tensors. These are used for
-            # for speculative decoding but we don't include them in this model
-            # conversion. See https://github.com/ggml-org/llama.cpp/pull/18886
-            if name.startswith("mtp."):
-                logger.info(f"gguf: Skipping MTP (Speculative) layer: {name}")
-                return
+    _MTP_SPECIAL_RENAMES = {
+        "mtp.layers.0.enorm.weight":           "model.layers.{bid}.enorm.weight",
+        "mtp.layers.0.hnorm.weight":           "model.layers.{bid}.hnorm.weight",
+        "mtp.layers.0.eh_proj.weight":         "model.layers.{bid}.eh_proj.weight",
+        "mtp.layers.1.norm.weight":            "model.layers.{bid}.post_attention_layernorm.weight",
+        "mtp.layers.1.final_layernorm.weight": "model.layers.{bid}.shared_head.norm.weight",
+    }
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        #   mtp.layers.0: NextN input fusion + attention
+        #   mtp.layers.1: MoE + final head norm
+        if self._mtp_bid is not None and name.startswith(("mtp.layers.0.", "mtp.layers.1.")):
+            suffix = name.split(".", 3)[3]
+            bid = self._mtp_bid
+            renamed = self._MTP_SPECIAL_RENAMES.get(name)
+            name = renamed.format(bid=bid) if renamed else f"backbone.layers.{bid}.{suffix}"
+
+        if self.is_moe and bid is not None:
             if name.endswith("mixer.gate.e_score_correction.bias"):
                 yield from ModelBase.modify_tensors(self, data_torch, name, bid)
                 return

conversion/pockettts.py

@@ -0,0 +1,378 @@
+from __future__ import annotations
+
+import re
+from pathlib import Path
+from typing import Any, Iterable, TYPE_CHECKING
+
+import torch
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, MmprojModel, SentencePieceTokenTypes, TextModel, gguf, logger
+
+# Pocket TTS is a CALM: the backbone conditions a flow-matching decoder that generates one
+# continuous 32-d latent per frame. There is no codebook in this model.
+# The checkpoint ships no config.json, hparams come from _load_hparams() below.
+#
+# Tricks being used to support this model via existing llama.cpp code paths:
+# - bos_before_voice and bos_emb are learned input vectors, not tokens
+#   they are appended to the embedding table as extra tokens, to be looked up like any other row
+# - bos_emb lives in latent space, so input_linear is folded into it here
+# - the backbone has no lm_head, the embedding table is reused as output for the unused logits
+#
+# pipeline stage mapping:
+#   mimi encoder + speaker_proj --> mapped to normal mtmd audio encoder
+#   flow_lm.transformer         --> mapped to normal libllama text model (autoregressive)
+#   flow_lm.flow_net + out_eos  --> MTMD_GEN_PROCESS_TYPE_GEN_CODE
+#   mimi decoder                --> MTMD_GEN_PROCESS_TYPE_GEN_WAV
+
+# indices into mimi.encoder.model / mimi.decoder.model for stage i, see SEANetEncoder/SEANetDecoder
+_ENC_RES_IDX   = lambda i: 1 + 3 * i  # noqa: E731
+_ENC_SCALE_IDX = lambda i: 3 + 3 * i  # noqa: E731
+_DEC_SCALE_IDX = lambda i: 2 + 3 * i  # noqa: E731
+_DEC_RES_IDX   = lambda i: 3 + 3 * i  # noqa: E731
+
+_N_SEANET_STAGES = 3
+_SAMPLE_RATE = 24000
+
+
+def _tensor_shapes(dir_model: Path) -> dict[str, tuple[int, ...]]:
+    part_names = ModelBase.get_model_part_names(dir_model, "model", ".safetensors")
+    if len(part_names) != 1:
+        return {}
+    with gguf.utility.SafetensorsLocal(dir_model / part_names[0]) as part:
+        return {name: tuple(part[name].shape) for name in part.keys()}
+
+
+@ModelBase.register_hparams_loader(lambda dir_model: "flow_lm.bos_emb" in _tensor_shapes(dir_model))
+def _load_hparams(dir_model: Path) -> dict[str, Any]:
+    logger.info("gguf: detected pocket-tts checkpoint, deriving hparams from tensor shapes")
+    shapes = _tensor_shapes(dir_model)
+    n_vocab, n_embd = shapes["flow_lm.conditioner.embed.weight"]
+    n_layer = sum(1 for name in shapes if re.fullmatch(r"flow_lm\.transformer\.layers\.\d+\.norm1\.weight", name))
+    n_layer_a = sum(1 for name in shapes if re.fullmatch(r"mimi\.encoder_transformer\.transformer\.layers\.\d+\.norm1\.weight", name))
+    n_embd_a = shapes["mimi.encoder_transformer.transformer.layers.0.norm1.weight"][0]
+    return {
+        "architectures": ["PocketTTSModel"],
+        "model_type": "pockettts",
+        "num_hidden_layers": n_layer,
+        "hidden_size": n_embd,
+        "intermediate_size": shapes["flow_lm.transformer.layers.0.linear1.weight"][0],
+        # the transformer is fully causal with no context limit, this only bounds the KV cache
+        "max_position_embeddings": 4096,
+        # not in the checkpoint, but every released variant uses head_dim 64
+        "num_attention_heads": n_embd // 64,
+        # extra rows for the learned input vectors, see _embd_table()
+        "vocab_size": n_vocab + (2 if "flow_lm.bos_before_voice" in shapes else 1),
+        "rope_theta": 10000.0,
+        "layer_norm_eps": 1e-5,
+        "audio_config": {
+            "num_hidden_layers": n_layer_a,
+            "hidden_size": n_embd_a,
+            "intermediate_size": shapes["mimi.encoder_transformer.transformer.layers.0.linear1.weight"][0],
+            "num_attention_heads": n_embd_a // 64,
+        },
+    }
+
+
+@ModelBase.register("PocketTTSModel")
+class PocketTTSModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.POCKETTTS
+
+    _LAYER_TENSOR_MAP = {
+        "norm1":               gguf.MODEL_TENSOR.ATTN_NORM,
+        "norm2":               gguf.MODEL_TENSOR.FFN_NORM,
+        "self_attn.out_proj":  gguf.MODEL_TENSOR.ATTN_OUT,
+        "linear1":             gguf.MODEL_TENSOR.FFN_UP,
+        "linear2":             gguf.MODEL_TENSOR.FFN_DOWN,
+    }
+
+    def set_vocab(self):
+        # this is a unigram sentencepiece model, llama.cpp's SPM tokenizer cannot do
+        # unigram segmentation, so use the UGM tokenizer instead
+        from sentencepiece import sentencepiece_model_pb2 as model
+
+        proto = model.ModelProto()  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        proto.ParseFromString(open(self.dir_model / "tokenizer.model", "rb").read())
+        assert proto.trainer_spec.model_type == 1, "expected a unigram tokenizer"
+
+        tokens, scores, toktypes = self._create_vocab_sentencepiece()
+
+        # the last rows of the embedding table are not sentencepiece pieces
+        extra = self._extra_tokens()
+        for i, name in enumerate(extra):
+            tokens[len(tokens) - len(extra) + i] = name.encode("utf-8")
+            toktypes[len(tokens) - len(extra) + i] = SentencePieceTokenTypes.CONTROL
+            scores[len(tokens) - len(extra) + i] = -1000.0
+
+        self.gguf_writer.add_tokenizer_model("t5")
+        self.gguf_writer.add_tokenizer_pre("default")
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_scores(scores)
+        self.gguf_writer.add_token_types(toktypes)
+        self.gguf_writer.add_add_space_prefix(proto.normalizer_spec.add_dummy_prefix)
+        self.gguf_writer.add_remove_extra_whitespaces(proto.normalizer_spec.remove_extra_whitespaces)
+        if proto.normalizer_spec.precompiled_charsmap:
+            self.gguf_writer.add_precompiled_charsmap(proto.normalizer_spec.precompiled_charsmap)
+        self.gguf_writer.add_add_bos_token(False)
+        self.gguf_writer.add_add_eos_token(False)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if not name.startswith("flow_lm."):
+            return  # mimi and the flow net go to the mmproj
+
+        if name == "flow_lm.conditioner.embed.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD), self._embd_table(data_torch))
+            return
+
+        if name.startswith("flow_lm.out_norm."):
+            suffix = "." + name.rsplit(".", 1)[1]
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM, suffix=suffix), data_torch)
+            return
+
+        if name.startswith("flow_lm.transformer.layers."):
+            assert bid is not None
+            key_with_suffix = name.split(f"layers.{bid}.", 1)[1]
+            key, suffix = key_with_suffix.rsplit(".", 1)
+
+            if key == "self_attn.in_proj":
+                q, k, v = data_torch.chunk(3, dim=0)
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q)
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k)
+                yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v)
+                return
+
+            tensor = self._LAYER_TENSOR_MAP.get(key)
+            if tensor is not None:
+                yield (self.format_tensor_name(tensor, bid, suffix="." + suffix), data_torch)
+                return
+
+        return
+
+    def _extra_tokens(self) -> list[str]:
+        # the conditioner's padding row, then the learned vectors appended by _embd_table().
+        # bos_before_voice only exists when the pack sets insert_bos_before_voice
+        names = ["<|pad|>"]
+        if "flow_lm.bos_before_voice" in self.model_tensors:
+            names.append("<|bos_before_voice|>")
+        names.append("<|audio_bos|>")
+        return names
+
+    def _embd_table(self, embed: Tensor) -> Tensor:
+        rows = [embed]
+        if "flow_lm.bos_before_voice" in self.model_tensors:
+            rows.append(self.model_tensors["flow_lm.bos_before_voice"]().reshape(1, -1).to(embed.dtype))
+
+        # bos_emb is a latent, it only enters the backbone through input_linear
+        bos_emb = self.model_tensors["flow_lm.bos_emb"]()
+        input_linear = self.model_tensors["flow_lm.input_linear.weight"]()
+        audio_bos = torch.nn.functional.linear(bos_emb.float(), input_linear.float()).reshape(1, -1)
+        rows.append(audio_bos.to(embed.dtype))
+
+        return torch.cat(rows, dim=0)
+
+
+@ModelBase.register("PocketTTSModel")
+class PocketTTSMmprojModel(MmprojModel):
+    has_audio_encoder = True
+    has_vision_encoder = False
+
+    _MIMI_TFM_MAP = {
+        "norm1":              (gguf.MODEL_TENSOR.A_ENC_INPUT_NORM,  gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_NORM),
+        "norm2":              (gguf.MODEL_TENSOR.A_ENC_OUTPUT_NORM, gguf.MODEL_TENSOR.A_GEN_WAV_TFM_FFN_NORM),
+        "self_attn.out_proj": (gguf.MODEL_TENSOR.A_ENC_OUTPUT,      gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_OUT),
+        "linear1":            (gguf.MODEL_TENSOR.A_ENC_FFN_UP,      gguf.MODEL_TENSOR.A_GEN_WAV_TFM_FFN_UP),
+        "linear2":            (gguf.MODEL_TENSOR.A_ENC_FFN_DOWN,    gguf.MODEL_TENSOR.A_GEN_WAV_TFM_FFN_DOWN),
+        "layer_scale_1.scale": (gguf.MODEL_TENSOR.A_ENC_ATTN_SCALE, gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_SCALE),
+        "layer_scale_2.scale": (gguf.MODEL_TENSOR.A_ENC_FFN_SCALE_LS, gguf.MODEL_TENSOR.A_GEN_WAV_TFM_FFN_SCALE),
+    }
+    _MIMI_TFM_QKV = (
+        (gguf.MODEL_TENSOR.A_ENC_ATTN_Q, gguf.MODEL_TENSOR.A_ENC_ATTN_K, gguf.MODEL_TENSOR.A_ENC_ATTN_V),
+        (gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_Q, gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_K, gguf.MODEL_TENSOR.A_GEN_WAV_TFM_ATTN_V),
+    )
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_file_type(self.ftype)
+        assert self.hparams_audio is not None
+
+        # voice-prompt encoder: mimi encoder + speaker_proj
+        self.gguf_writer.add_clip_has_audio_encoder(True)
+        # note: the 24kHz sample rate is hardcoded on the clip.cpp side, like the other audio models
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.POCKETTTS_SPKENC)
+        self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
+        self.gguf_writer.add_audio_block_count(self.hparams_audio["num_hidden_layers"])
+        self.gguf_writer.add_audio_embedding_length(self.hparams_audio["hidden_size"])
+        self.gguf_writer.add_audio_feed_forward_length(self.hparams_audio["intermediate_size"])
+        self.gguf_writer.add_audio_head_count(self.hparams_audio["num_attention_heads"])
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+        # mimi convolves the waveform directly, it is passed around as a 1-row "mel"
+        self.gguf_writer.add_audio_num_mel_bins(1)
+
+        # generation: flow-matching decoder + mimi decoder
+        # the SEANet and flow net hparams are constant across the family, clip.cpp holds them
+        self.gguf_writer.add_clip_has_gen_audio_encoder(True)
+        self.gguf_writer.add_clip_gen_audio_projector_type(gguf.VisionProjectorType.POCKETTTS_GEN)
+        self.gguf_writer.add_gen_audio_projection_dim(self.n_embd_text)
+        self.gguf_writer.add_gen_audio_embedding_length(self.hparams_audio["hidden_size"])
+        self.gguf_writer.add_gen_audio_feed_forward_length(self.hparams_audio["intermediate_size"])
+        self.gguf_writer.add_gen_audio_block_count(self.hparams_audio["num_hidden_layers"])
+        self.gguf_writer.add_gen_audio_head_count(self.hparams_audio["num_attention_heads"])
+        self.gguf_writer.add_gen_audio_attention_layernorm_eps(1e-5)
+
+        self.gguf_writer.add_gen_audio_model_variant(self.dir_model.name)
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        del name, bid, n_dims
+        # conv1d/conv1d_dw kernels must be F16, ggml_conv_1d(_dw) has no BF16 path
+        if ".seanet." in new_name or new_name in ("a.downsample.conv.weight", "a.gen.wav.upsample.weight"):
+            return gguf.GGMLQuantizationType.F16
+        return False
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        del bid  # the block index of the mimi transformers is parsed here, not by the base class
+        T = gguf.MODEL_TENSOR
+
+        if name in ("flow_lm.bos_emb", "flow_lm.bos_before_voice", "flow_lm.conditioner.embed.weight"):
+            return  # folded into the backbone embedding table
+        if name.startswith("flow_lm.transformer.") or name.startswith("flow_lm.out_norm."):
+            return  # backbone
+
+        if name == "flow_lm.speaker_proj_weight":
+            yield (self.format_tensor_name(T.A_ENC_SPEAKER_PROJ), data_torch)
+            return
+        if name == "flow_lm.input_linear.weight":
+            yield (self.format_tensor_name(T.A_GEN_INPUT_LINEAR), data_torch)
+            return
+        if name == "flow_lm.emb_mean":
+            yield (self.format_tensor_name(T.A_GEN_EMB_MEAN, suffix=""), data_torch)
+            return
+        if name == "flow_lm.emb_std":
+            yield (self.format_tensor_name(T.A_GEN_EMB_STD, suffix=""), data_torch)
+            return
+        if name.startswith("flow_lm.out_eos."):
+            suffix = "." + name.rsplit(".", 1)[1]
+            yield (self.format_tensor_name(T.A_GEN_OUT_EOS, suffix=suffix), data_torch)
+            return
+
+        if name.startswith("flow_lm.flow_net."):
+            yield from self._flow_net_tensor(name, data_torch)
+            return
+
+        if name == "mimi.downsample.conv.conv.weight":
+            yield (self.format_tensor_name(T.A_ENC_DOWNSAMPLE_CONV), data_torch)
+            return
+        if name == "mimi.upsample.convtr.convtr.weight":
+            yield (self.format_tensor_name(T.A_GEN_WAV_UPSAMPLE), data_torch)
+            return
+        if name == "mimi.quantizer.output_proj.weight":
+            yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_OUT), data_torch.squeeze(-1))
+            return
+
+        if "_transformer.transformer.layers." in name:
+            yield from self._mimi_tfm_tensor(name, data_torch)
+            return
+
+        if name.startswith("mimi.encoder.model.") or name.startswith("mimi.decoder.model."):
+            yield from self._seanet_tensor(name, data_torch)
+            return
+
+        return
+
+    def _flow_net_tensor(self, name: str, data_torch: Tensor) -> Iterable[tuple[str, Tensor]]:
+        T = gguf.MODEL_TENSOR
+        key = name.split("flow_lm.flow_net.", 1)[1]
+        suffix = "." + key.rsplit(".", 1)[1]
+
+        simple = {
+            "input_proj":                 T.A_GEN_FLOW_INPUT_PROJ,
+            "cond_embed":                 T.A_GEN_FLOW_COND_EMBD,
+            "final_layer.linear":         T.A_GEN_FLOW_FINAL_PROJ,
+            "final_layer.adaLN_modulation.1": T.A_GEN_FLOW_FINAL_ADA,
+        }
+        tensor = simple.get(key.rsplit(".", 1)[0])
+        if tensor is not None:
+            yield (self.format_tensor_name(tensor, suffix=suffix), data_torch)
+            return
+
+        if key.startswith("time_embed."):
+            bid = int(key.split(".")[1])
+            rest = key.split(f"time_embed.{bid}.", 1)[1]
+            time_map = {
+                "freqs":       (T.A_GEN_FLOW_TIME_FREQS, ""),
+                "mlp.0":       (T.A_GEN_FLOW_TIME_UP,    suffix),
+                "mlp.2":       (T.A_GEN_FLOW_TIME_DOWN,  suffix),
+                "mlp.3.alpha": (T.A_GEN_FLOW_TIME_NORM,  ""),
+            }
+            entry = time_map.get(rest) or time_map.get(rest.rsplit(".", 1)[0])
+            if entry is not None:
+                yield (self.format_tensor_name(entry[0], bid, suffix=entry[1]), data_torch)
+            return
+
+        if key.startswith("res_blocks."):
+            bid = int(key.split(".")[1])
+            rest = key.split(f"res_blocks.{bid}.", 1)[1].rsplit(".", 1)[0]
+            blk_map = {
+                "in_ln":                T.A_GEN_FLOW_BLK_NORM,
+                "mlp.0":                T.A_GEN_FLOW_BLK_UP,
+                "mlp.2":                T.A_GEN_FLOW_BLK_DOWN,
+                "adaLN_modulation.1":   T.A_GEN_FLOW_BLK_ADA,
+            }
+            tensor = blk_map.get(rest)
+            if tensor is not None:
+                yield (self.format_tensor_name(tensor, bid, suffix=suffix), data_torch)
+            return
+
+    def _mimi_tfm_tensor(self, name: str, data_torch: Tensor) -> Iterable[tuple[str, Tensor]]:
+        is_decoder = name.startswith("mimi.decoder_transformer.")
+        bid = int(name.split("_transformer.transformer.layers.", 1)[1].split(".")[0])
+        key_with_suffix = name.split(f".layers.{bid}.", 1)[1]
+
+        if key_with_suffix == "self_attn.in_proj.weight":
+            q, k, v = data_torch.chunk(3, dim=0)
+            names = self._MIMI_TFM_QKV[1 if is_decoder else 0]
+            for tensor, part in zip(names, (q, k, v)):
+                yield (self.format_tensor_name(tensor, bid), part)
+            return
+
+        key, suffix = key_with_suffix.rsplit(".", 1)
+        entry = self._MIMI_TFM_MAP.get(key) or self._MIMI_TFM_MAP.get(key_with_suffix)
+        if entry is None:
+            return
+        tensor = entry[1 if is_decoder else 0]
+        suffix = ".weight" if key_with_suffix.endswith(".scale") else "." + suffix
+        yield (self.format_tensor_name(tensor, bid, suffix=suffix), data_torch)
+
+    def _seanet_tensor(self, name: str, data_torch: Tensor) -> Iterable[tuple[str, Tensor]]:
+        T = gguf.MODEL_TENSOR
+        is_decoder = name.startswith("mimi.decoder.")
+        idx = int(name.split(".model.", 1)[1].split(".")[0])
+        suffix = "." + name.rsplit(".", 1)[1]
+
+        conv_in, conv_out, res1, res2, scale = (
+            (T.A_GEN_WAV_SEANET_CONV_IN, T.A_GEN_WAV_SEANET_CONV_OUT, T.A_GEN_WAV_SEANET_RES_CONV1,
+             T.A_GEN_WAV_SEANET_RES_CONV2, T.A_GEN_WAV_SEANET_SCALE_CONV)
+            if is_decoder else
+            (T.A_ENC_SEANET_CONV_IN, T.A_ENC_SEANET_CONV_OUT, T.A_ENC_SEANET_RES_CONV1,
+             T.A_ENC_SEANET_RES_CONV2, T.A_ENC_SEANET_SCALE_CONV)
+        )
+
+        if idx == 0:
+            yield (self.format_tensor_name(conv_in, suffix=suffix), data_torch)
+            return
+        if idx == 3 * _N_SEANET_STAGES + 2:
+            yield (self.format_tensor_name(conv_out, suffix=suffix), data_torch)
+            return
+
+        for stage in range(_N_SEANET_STAGES):
+            res_idx = _DEC_RES_IDX(stage) if is_decoder else _ENC_RES_IDX(stage)
+            scale_idx = _DEC_SCALE_IDX(stage) if is_decoder else _ENC_SCALE_IDX(stage)
+            if idx == scale_idx:
+                yield (self.format_tensor_name(scale, stage, suffix=suffix), data_torch)
+                return
+            if idx == res_idx:
+                # block.1 is the dilated conv, block.3 the pointwise one (0 and 2 are ELU)
+                inner = int(name.split(".block.", 1)[1].split(".")[0])
+                tensor = res1 if inner == 1 else res2
+                yield (self.format_tensor_name(tensor, stage, suffix=suffix), data_torch)
+                return

conversion/qwen.py

@@ -18,7 +18,7 @@ class QwenModel(TextModel):
 
     @staticmethod
     def token_bytes_to_string(b):
-        from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode  # ty: ignore[unresolved-import]
+        from transformers.convert_slow_tokenizer import bytes_to_unicode
         byte_encoder = bytes_to_unicode()
         return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
 
@@ -268,8 +268,101 @@ class Qwen3MoeModel(Qwen2MoeModel):
         super().set_vocab()
 
 
+class _QwenMtpMixin:
+    """Shared MTP wiring for Qwen3-Next and Qwen3.5/3.6 text variants. The HF
+    config carries the MTP block under `mtp_num_hidden_layers` (computed from
+    the checkpoint when absent, e.g. Qwen3-Next) and the tensors under
+    `mtp.*`; we extend block_count, emit the nextn metadata key, and remap
+    `mtp.*` to the standard layer-indexed nextn naming so the existing
+    tensor_map handles them."""
+
+    supports_mtp_export = True
+    hparams: dict[str, Any]
+    model_arch: gguf.MODEL_ARCH
+    gguf_writer: gguf.GGUFWriter
+    block_count: int
+    tensor_map: gguf.TensorNameMap
+    no_mtp: bool
+    mtp_only: bool
+    _original_block_count: int | None = None
+    opt_num_mtp_layers: int = 0
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.block_count = self.hparams["num_hidden_layers"]
+        if not self.no_mtp:
+            n_mtp = self.hparams.get("mtp_num_hidden_layers", 0)
+            # Qwen-3-Next doesn't include `mtp_num_hidden_layers` in config.
+            if n_mtp == 0:
+                assert self.opt_num_mtp_layers != 0
+                n_mtp = self.opt_num_mtp_layers
+            self.block_count += n_mtp
+        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+
+    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+        hparams = {**self.hparams, **self.hparams.get("text_config", {})}
+        key = next((k for k in ["n_layers", "num_hidden_layers", "n_layer", "num_layers"] if k in hparams), None)
+        type(self)._original_block_count = hparams.get(key)
+        type(self).opt_num_mtp_layers = 0
+        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)  # ty: ignore[unresolved-attribute]
+
+    @classmethod
+    def filter_tensors(cls, item):
+        assert cls._original_block_count is not None
+        # TODO: change TextModel to super()
+        if (titem := TextModel.filter_tensors(item)) is None:
+            return None
+        name, gen = titem
+        if name.startswith("model.mtp."):
+            name = name.replace("model.", "", 1)
+        if name.startswith("mtp."):
+            if cls.no_mtp:
+                return None
+            remapper = {
+                "fc":                    "eh_proj",
+                "pre_fc_norm_embedding": "enorm",
+                "pre_fc_norm_hidden":    "hnorm",
+                "norm":                  "shared_head.norm",
+            }
+            parts = name.split(".", 3)
+            if len(parts) == 4 and parts[1] == "layers" and parts[2].isdecimal():
+                mtp_idx = int(parts[2])
+                name = f"model.layers.{cls._original_block_count + mtp_idx}.{parts[3]}"
+                cls.opt_num_mtp_layers = max(cls.opt_num_mtp_layers, mtp_idx + 1)
+            elif len(parts) == 3 and parts[1] in remapper:
+                name = f"model.layers.{cls._original_block_count}.{remapper[parts[1]]}.{parts[2]}"
+        elif cls.mtp_only:
+            keep = name in (
+                "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
+                "embed_tokens.weight", "norm.weight",
+            )
+            if not keep:
+                return None
+        return name, gen
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()  # ty: ignore[unresolved-attribute]
+        if self.no_mtp:
+            return
+        if (n := self.block_count - self.hparams["num_hidden_layers"]) > 0:
+            self.gguf_writer.add_nextn_predict_layers(n)
+
+    def prepare_metadata(self, vocab_only: bool):
+        from_dir = self.fname_out.is_dir()
+        super().prepare_metadata(vocab_only=vocab_only)  # ty: ignore[unresolved-attribute]
+
+        if not self.mtp_only or not from_dir:
+            return
+
+        output_type: str = self.ftype.name.partition("_")[2]  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        fname_default: str = gguf.naming_convention(
+            self.metadata.name, self.metadata.basename, self.metadata.finetune,                  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+            self.metadata.version, size_label=None, output_type=output_type, model_type=None)    # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
+        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
+
+
 @ModelBase.register("Qwen3NextForCausalLM")
-class Qwen3NextModel(Qwen2MoeModel):
+class Qwen3NextModel(_QwenMtpMixin, Qwen2MoeModel):
     model_arch = gguf.MODEL_ARCH.QWEN3NEXT
 
     def set_gguf_parameters(self):
@@ -284,16 +377,6 @@ class Qwen3NextModel(Qwen2MoeModel):
             rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"]
         self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.rope_parameters.get("partial_rotary_factor", 0.25)))
 
-    @classmethod
-    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
-        name, gen = item
-
-        if name.startswith("mtp"):
-            # ignore MTP layers for now
-            return None
-
-        return super().filter_tensors(item)
-
     def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
         if name.endswith(".A_log"):
             data_torch = -torch.exp(data_torch)
@@ -536,97 +619,13 @@ class _Qwen35MRopeMixin:
             self.gguf_writer.add_rope_dimension_sections(self._QWEN35_DEFAULT_MROPE_SECTION)
 
 
-class _Qwen35MtpMixin:
-    """Shared MTP wiring for Qwen3.5/3.6 text variants. The HF config carries
-    the MTP block under `mtp_num_hidden_layers` and the tensors under
-    `mtp.*`; we extend block_count, emit the nextn metadata key, and remap
-    `mtp.*` to the standard layer-indexed nextn naming so the existing
-    tensor_map handles them."""
-
-    supports_mtp_export = True
-    hparams: dict[str, Any]
-    model_arch: gguf.MODEL_ARCH
-    gguf_writer: gguf.GGUFWriter
-    block_count: int
-    tensor_map: gguf.TensorNameMap
-    no_mtp: bool
-    mtp_only: bool
-    _original_block_count: int | None = None
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.block_count = self.hparams["num_hidden_layers"]
-        if not self.no_mtp:
-            self.block_count += self.hparams.get("mtp_num_hidden_layers", 0)
-        self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
-
-    def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
-        hparams = {**self.hparams, **self.hparams.get("text_config", {})}
-        key = next((k for k in ["n_layers", "num_hidden_layers", "n_layer", "num_layers"] if k in hparams), None)
-        type(self)._original_block_count = hparams.get(key)
-        return super().index_tensors(remote_hf_model_id=remote_hf_model_id)  # ty: ignore[unresolved-attribute]
-
-    @classmethod
-    def filter_tensors(cls, item):
-        assert cls._original_block_count is not None
-        # TODO: change TextModel to super()
-        if (titem := TextModel.filter_tensors(item)) is None:
-            return None
-        name, gen = titem
-        if name.startswith("model.mtp."):
-            name = name.replace("model.", "", 1)
-        if name.startswith("mtp."):
-            if cls.no_mtp:
-                return None
-            remapper = {
-                "fc":                    "eh_proj",
-                "pre_fc_norm_embedding": "enorm",
-                "pre_fc_norm_hidden":    "hnorm",
-                "norm":                  "shared_head.norm",
-            }
-            parts = name.split(".", 3)
-            if len(parts) == 4 and parts[1] == "layers" and parts[2].isdecimal():
-                mtp_idx = int(parts[2])
-                name = f"model.layers.{cls._original_block_count + mtp_idx}.{parts[3]}"
-            elif len(parts) == 3 and parts[1] in remapper:
-                name = f"model.layers.{cls._original_block_count}.{remapper[parts[1]]}.{parts[2]}"
-        elif cls.mtp_only:
-            keep = name in (
-                "model.embed_tokens.weight", "model.norm.weight", "lm_head.weight",
-                "embed_tokens.weight", "norm.weight",
-            )
-            if not keep:
-                return None
-        return name, gen
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()  # ty: ignore[unresolved-attribute]
-        if self.no_mtp:
-            return
-        if (n := self.hparams.get("mtp_num_hidden_layers", 0)) > 0:
-            self.gguf_writer.add_nextn_predict_layers(n)
-
-    def prepare_metadata(self, vocab_only: bool):
-        from_dir = self.fname_out.is_dir()
-        super().prepare_metadata(vocab_only=vocab_only)  # ty: ignore[unresolved-attribute]
-
-        if not self.mtp_only or not from_dir:
-            return
-
-        output_type: str = self.ftype.name.partition("_")[2]  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
-        fname_default: str = gguf.naming_convention(
-            self.metadata.name, self.metadata.basename, self.metadata.finetune,                  # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
-            self.metadata.version, size_label=None, output_type=output_type, model_type=None)    # pyright: ignore[reportAttributeAccessIssue] # ty: ignore[unresolved-attribute]
-        self.fname_out = self.fname_out.parent / f"mtp-{fname_default}.gguf"
-
-
 @ModelBase.register("Qwen3_5ForConditionalGeneration", "Qwen3_5ForCausalLM")
-class Qwen3_5TextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase):
+class Qwen3_5TextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
     model_arch = gguf.MODEL_ARCH.QWEN35
 
 
 @ModelBase.register("Qwen3_5MoeForConditionalGeneration", "Qwen3_5MoeForCausalLM")
-class Qwen3_5MoeTextModel(_Qwen35MtpMixin, _Qwen35MRopeMixin, _LinearAttentionVReorderBase):
+class Qwen3_5MoeTextModel(_Qwen35MRopeMixin, _LinearAttentionVReorderBase):
     model_arch = gguf.MODEL_ARCH.QWEN35MOE
 
 
@@ -648,10 +647,13 @@ class DFlashModel(Qwen3Model):
         # own tokenizer logic, not the Qwen default).
         from . import get_model_class
         with open(self.target_model_dir / "config.json", "r", encoding="utf-8") as f:
-            target_arch = json.load(f)["architectures"][0]
+            target_hparams = json.load(f)
+            target_arch = target_hparams["architectures"][0]
         target_cls = get_model_class(target_arch)
 
         if target_cls is not type(self):
+            if target_arch == "NemotronHForCausalLM":
+                setattr(self, "is_moe", "num_experts_per_tok" in target_hparams)
             target_cls.set_vocab(self)  # ty: ignore[unresolved-attribute]
         else:
             super().set_vocab()
@@ -689,6 +691,12 @@ class DFlashModel(Qwen3Model):
             name = "model." + name
         return super().filter_tensors((name, gen))
 
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        if name == "model.embed_tokens.weight" and not self.hparams.get("has_embed_tokens", True):
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
 
 @ModelBase.register("Qwen3DSparkModel")
 class DSparkModel(DFlashModel):

conversion/qwen3tts.py

@@ -0,0 +1,471 @@
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, Callable, Iterable, TYPE_CHECKING
+
+import torch
+import torch.nn.functional as F
+
+if TYPE_CHECKING:
+    from torch import Tensor
+
+from .base import ModelBase, MmprojModel, TextModel, gguf
+
+# Tricks being used to support this model via existing llama.cpp code paths:
+# - Text projection MLP is folded into the embedding table
+# - codec_embedding is concat to the text embedding table, vocab is extended
+#   example: codec_bos_id(2149) --> "<|codec_bos|>"
+#            codec_eos_token_id(2150) --> "<|codec_eos_token|>"
+#            codec_language_id.chinese(2055) --> "<|codec_language_chinese|>"
+#            other rows --> "<|codec_0|>", "<|codec_1|>", ..., "<|codec_1023|>"
+# - output tensor codec_head is smaller than vocab, so logits will be padded at inference time
+# - suppress_tokens is used to limit the backbone to only sample either semantic or EOS (stop) token
+
+# pipeline stage mapping:
+#   speaker reference encoder --> mapped to normal mtmd audio encoder
+#   backbone --> mapped to normal libllama text model (autoregressive)
+#   code_predictor --> MTMD_GEN_PROCESS_TYPE_GEN_CODE
+#   code2wav --> MTMD_GEN_PROCESS_TYPE_GEN_WAV
+
+# torch activation functions used by Qwen3TTSTalkerResizeMLP (config's hidden_act)
+_ACT2FN = {
+    "silu": F.silu,
+    "gelu": F.gelu,
+    "relu": F.relu,
+}
+
+
+@ModelBase.register("Qwen3TTSForConditionalGeneration")
+class Qwen3TTSTalkerModel(TextModel):
+    model_arch = gguf.MODEL_ARCH.QWEN3TTS
+
+    _TEXT_PROJ_KEYS = (
+        "model.text_embedding.weight",
+        "text_projection.linear_fc1.weight",
+        "text_projection.linear_fc1.bias",
+        "text_projection.linear_fc2.weight",
+        "text_projection.linear_fc2.bias",
+    )
+
+    _text_proj_buffer: dict[str, Tensor]
+    _folded_text_embed: Tensor | None
+    _codec_embed: Tensor | None
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False)
+        raw_talker_config = dict(hparams["talker_config"])
+        self._talker_config = raw_talker_config
+        self.n_codec_vocab = raw_talker_config["vocab_size"]
+        talker_config = dict(raw_talker_config)
+        talker_config["vocab_size"] = talker_config["text_vocab_size"]
+        hparams["text_config"] = talker_config
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+        self._text_proj_buffer = {}
+        self._folded_text_embed = None
+        self._codec_embed = None
+
+    def _codec_token_names(self) -> list[str]:
+        # start every row with a generic name, then override the ones with a
+        # known meaning (bos/eos/language/etc, derived from the *_id fields
+        # of talker_config) with a more descriptive one
+        names = [f"<|codec_{i}|>" for i in range(self.n_codec_vocab)]
+        for key, val in self._talker_config.items():
+            if not key.endswith("_id"):
+                continue
+            prefix = key[:-len("_id")]
+            if isinstance(val, int):
+                names[val] = f"<|{prefix}|>"
+            elif isinstance(val, dict):
+                for subkey, subval in val.items():
+                    names[subval] = f"<|{prefix}_{subkey}|>"
+        return names
+
+    def set_vocab(self):
+        codec_tokens = self._codec_token_names()
+        codec_toktypes = [gguf.TokenType.CONTROL] * len(codec_tokens)
+
+        try:
+            tokens, scores, toktypes = self._create_vocab_sentencepiece()
+            self.gguf_writer.add_tokenizer_model("llama")
+            self.gguf_writer.add_tokenizer_pre("default")
+            tokens += [t.encode("utf-8") for t in codec_tokens]
+            scores += [0.0] * len(codec_tokens)
+            toktypes += codec_toktypes
+            self.gguf_writer.add_token_list(tokens)
+            self.gguf_writer.add_token_scores(scores)
+            self.gguf_writer.add_token_types(toktypes)
+            special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens))
+            special_vocab.add_to_gguf(self.gguf_writer)
+            return
+        except FileNotFoundError:
+            pass
+
+        tokens, toktypes, tokpre = self.get_vocab_base()
+        tokens += codec_tokens
+        toktypes += codec_toktypes
+        self.gguf_writer.add_tokenizer_model("gpt2")
+        self.gguf_writer.add_tokenizer_pre(tokpre)
+        self.gguf_writer.add_token_list(tokens)
+        self.gguf_writer.add_token_types(toktypes)
+
+        special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
+        special_vocab.add_to_gguf(self.gguf_writer)
+
+        # make sure that the model has no chat template, so chat will be disabled
+        self.gguf_writer.add_chat_template(None)
+
+    def set_gguf_parameters(self):
+        super().set_gguf_parameters()
+
+        # note: final vocab layout is [text_vocab | codec_vocab], with text_vocab is actually padded with -inf in cgraph
+        # for codec_vocab, only first 2048 rows can be sampled for semantic code
+        # plus codec_eos_token_id that used for signaling end of generation
+        # ref: https://github.com/QwenLM/Qwen3-TTS/blob/022e286b98fbec7e1e916cb940cdf532cd9f488e/qwen_tts/core/models/modeling_qwen3_tts.py#L2059-L2063
+
+        vocab_size = self.hparams["vocab_size"] + self.n_codec_vocab
+        codec_eos_token_id = self.hparams["vocab_size"] + self._talker_config["codec_eos_token_id"]
+        self.gguf_writer.add_suppress_tokens([
+            i for i in range(vocab_size - 1024, vocab_size)
+            if i != codec_eos_token_id
+        ])
+        self.gguf_writer.add_eos_token_id(codec_eos_token_id)
+        self.gguf_writer.add_add_eos_token(False)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not name.startswith("talker.") or name.startswith("talker.code_predictor."):
+            return None
+
+        name = name[len("talker."):]
+        return super().filter_tensors((name, gen))
+
+    def _maybe_emit_token_embd(self) -> Iterable[tuple[str, Tensor]]:
+        if self._folded_text_embed is None or self._codec_embed is None:
+            return
+        combined = torch.cat([self._folded_text_embed, self._codec_embed], dim=0)
+        yield (self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD), combined)
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # codec_embedding rows are appended after the text vocab, extending the embedding table
+        if name == "model.codec_embedding.weight":
+            self._codec_embed = data_torch
+            yield from self._maybe_emit_token_embd()
+            return
+
+        # codec_head is the output head for the (smaller) codec vocab; logits get padded to
+        # the extended vocab size at inference time
+        if name == "codec_head.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)
+            return
+
+        if name in self._TEXT_PROJ_KEYS:
+            self._text_proj_buffer[name] = data_torch
+            if len(self._text_proj_buffer) < len(self._TEXT_PROJ_KEYS):
+                return
+
+            # fold MLP into the embedding table at conversion time, MLP won't be used at inference time anyway
+            act_fn = _ACT2FN[self.hparams["hidden_act"]]
+            embed = self._text_proj_buffer["model.text_embedding.weight"]
+            hidden = act_fn(F.linear(embed,
+                                     self._text_proj_buffer["text_projection.linear_fc1.weight"],
+                                     self._text_proj_buffer["text_projection.linear_fc1.bias"]))
+            folded = F.linear(hidden,
+                              self._text_proj_buffer["text_projection.linear_fc2.weight"],
+                              self._text_proj_buffer["text_projection.linear_fc2.bias"])
+            self._folded_text_embed = folded
+            yield from self._maybe_emit_token_embd()
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+
+@ModelBase.register("Qwen3TTSForConditionalGeneration")
+class Qwen3TTSSpeakerEncoderModel(MmprojModel):
+    has_vision_encoder = False
+    has_audio_encoder = True
+
+    # talker.code_predictor.model.layers.{bid}.<key> -> A_GEN_CODE_*
+    # bypass tensor_mapping.py for now to make it simple
+    _CODE_LAYER_TENSOR_MAP = {
+        "input_layernorm": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_NORM,
+        "self_attn.q_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_Q,
+        "self_attn.q_norm": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_Q_NORM,
+        "self_attn.k_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_K,
+        "self_attn.k_norm": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_K_NORM,
+        "self_attn.v_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_V,
+        "self_attn.o_proj": gguf.MODEL_TENSOR.A_GEN_CODE_ATTN_OUT,
+        "post_attention_layernorm": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_NORM,
+        "mlp.gate_proj": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_GATE,
+        "mlp.up_proj": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_UP,
+        "mlp.down_proj": gguf.MODEL_TENSOR.A_GEN_CODE_FFN_DOWN,
+    }
+
+    # note: codebook pages will be stacked to 3D
+    _CODE_GEN_N_CODEBOOKS = 15
+    _code_embed_buffer: dict[int, Tensor] = {}
+    _code_head_buffer: dict[int, Tensor] = {}
+    _wav_config_cache: dict[str, Any] | None = None
+
+    def __init__(self, dir_model: Path, *args, **kwargs):
+        hparams = kwargs.pop("hparams", None)
+        if hparams is None:
+            hparams = ModelBase.load_hparams(dir_model, is_mistral_format=False)
+        hparams["text_config"] = {"hidden_size": hparams["talker_config"]["hidden_size"]}
+        # ECAPA-TDNN has a fixed 4-stage backbone, but MmprojModel.__init__ needs a n_block_keys
+        hparams["speaker_encoder_config"]["n_layers"] = 4
+        super().__init__(dir_model, *args, hparams=hparams, **kwargs)
+        self._wav_config_cache = None
+
+    def get_audio_config(self) -> dict[str, Any] | None:
+        return self.global_config.get("speaker_encoder_config")
+
+    def set_gguf_parameters(self):
+        self.gguf_writer.add_file_type(self.ftype)
+        self.gguf_writer.add_clip_has_audio_encoder(True)
+        self.gguf_writer.add_clip_audio_projector_type(gguf.VisionProjectorType.QWEN3TTS_SPKENC)
+
+        # handle speaker encoder config
+        self.gguf_writer.add_audio_projection_dim(self.n_embd_text)
+        # mel_spectrogram() front-end: sr=24000, n_fft=1024, hop=256, n_mels=128, fmin=0, fmax=12000 (=sr/2, the clip.cpp default)
+        self.gguf_writer.add_audio_num_mel_bins(128)
+        # 3 SE-Res2Net stages; the stem conv, mfa, asp and fc are not counted here
+        self.gguf_writer.add_audio_block_count(3)
+        # ECAPA-TDNN has no attention/FFN, these are dummy to allow clip.cpp to load it
+        self.gguf_writer.add_audio_embedding_length(1536)
+        self.gguf_writer.add_audio_head_count(1)
+        self.gguf_writer.add_audio_feed_forward_length(1536)
+        self.gguf_writer.add_audio_attention_layernorm_eps(1e-5)
+
+        # handle code predictor config
+        self.gguf_writer.add_clip_has_gen_audio_encoder(True)
+        self.gguf_writer.add_clip_gen_audio_projector_type(gguf.VisionProjectorType.QWEN3TTS_GEN)
+        code_predictor_config = self.global_config["talker_config"]["code_predictor_config"]
+        self.gguf_writer.add_gen_audio_projection_dim(self.n_embd_text)
+        self.gguf_writer.add_gen_audio_embedding_length(code_predictor_config["hidden_size"])
+        self.gguf_writer.add_gen_audio_feed_forward_length(code_predictor_config["intermediate_size"])
+        self.gguf_writer.add_gen_audio_block_count(code_predictor_config["num_hidden_layers"])
+        self.gguf_writer.add_gen_audio_head_count(code_predictor_config["num_attention_heads"])
+        self.gguf_writer.add_gen_audio_head_count_kv(code_predictor_config["num_key_value_heads"])
+        self.gguf_writer.add_gen_audio_attention_layernorm_eps(code_predictor_config["rms_norm_eps"])
+        # note: code2wav hparams are hardcoded on the mtmd/clip.cpp side for now, not written here
+
+    def _wav_decoder_config(self) -> dict[str, Any] | None:
+        # code2wav has its own config.json, inside the speech_tokenizer dir
+        if self._wav_config_cache is None:
+            path = self.dir_model / "speech_tokenizer" / "config.json"
+            with open(path, "r", encoding="utf-8") as f:
+                cfg = json.load(f)
+            self._wav_config_cache = cfg["decoder_config"]
+        return self._wav_config_cache
+
+    def tensor_force_quant(self, name, new_name, bid, n_dims):
+        # conv1d/conv1d_dw kernels must be F16, ggml_conv_1d(_dw) has no BF16 path
+        if new_name.endswith(".weight") and (
+            new_name in ("a.gen.wav.pre_conv.weight", "a.gen.wav.dac.entry.weight", "a.gen.wav.dac.post_conv.weight")
+            or (".up.blk." in new_name and new_name.endswith(".dwconv.weight"))
+            or (".dac.blk." in new_name and (new_name.endswith(".conv1.weight") or new_name.endswith(".conv2.weight")))
+        ):
+            return gguf.GGMLQuantizationType.F16
+        # ConvTranspose1d kernels: only F16/F32 are implemented, no BF16
+        if new_name.endswith(".conv.weight") and (".up.blk." in new_name or ".dac.blk." in new_name):
+            return gguf.GGMLQuantizationType.F32
+        return super().tensor_force_quant(name, new_name, bid, n_dims)
+
+    @classmethod
+    def filter_tensors(cls, item: tuple[str, Callable[[], Tensor]]) -> tuple[str, Callable[[], Tensor]] | None:
+        name, gen = item
+
+        if not (
+            name.startswith("speaker_encoder.")
+            or name.startswith("talker.code_predictor.")
+            or name == "talker.model.codec_embedding.weight"
+        ):
+            return None
+
+        return super().filter_tensors((name, gen))
+
+    def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+        # code2wav tensors are already named by generate_extra_tensors(), pass them through
+        if name.startswith("a.gen.wav."):
+            yield (name, data_torch)
+            return
+
+        # codebook-0 embedding, fed back to the talker backbone (codebooks 1-15 live in code_predictor)
+        if name == "talker.model.codec_embedding.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_OUT_EMBD), data_torch)
+            return
+
+        if name == "talker.code_predictor.model.norm.weight":
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_OUTPUT_NORM), data_torch)
+            return
+
+        if name.startswith("talker.code_predictor.small_to_mtp_projection."):
+            suffix = "." + name.rsplit(".", 1)[1]
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_PROJ_IN, suffix=suffix), data_torch)
+            return
+
+        if name.startswith("talker.code_predictor.model.codec_embedding."):
+            idx = int(name.split("codec_embedding.")[1].split(".")[0])
+            self._code_embed_buffer[idx] = data_torch
+            if len(self._code_embed_buffer) < self._CODE_GEN_N_CODEBOOKS:
+                return
+            stacked = torch.stack([self._code_embed_buffer.pop(i) for i in range(self._CODE_GEN_N_CODEBOOKS)], dim=0)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_EMBD), stacked)
+            return
+
+        if name.startswith("talker.code_predictor.lm_head."):
+            idx = int(name.split("lm_head.")[1].split(".")[0])
+            self._code_head_buffer[idx] = data_torch
+            if len(self._code_head_buffer) < self._CODE_GEN_N_CODEBOOKS:
+                return
+            stacked = torch.stack([self._code_head_buffer.pop(i) for i in range(self._CODE_GEN_N_CODEBOOKS)], dim=0)
+            yield (self.format_tensor_name(gguf.MODEL_TENSOR.A_GEN_CODE_HEAD), stacked)
+            return
+
+        if name.startswith("talker.code_predictor.model.layers."):
+            rest = name.split("model.layers.")[1]        # "{bid}.<key>.weight"
+            _, key_with_suffix = rest.split(".", 1)       # "<key>.weight"
+            key = key_with_suffix.rsplit(".", 1)[0]        # "<key>"
+            tensor = self._CODE_LAYER_TENSOR_MAP.get(key)
+            if tensor is not None:
+                yield (self.format_tensor_name(tensor, bid), data_torch)
+                return
+
+        if "res2net_block.blocks." in name:
+            assert bid is not None  # the outer stage index, picked up from the tensor name automatically
+            xid = int(name.split("res2net_block.blocks.")[1].split(".")[0])
+            suffix = "." + name.rsplit(".", 1)[1]
+            new_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.A_ENC_CONV_RES2].format(bid=bid, xid=xid) + suffix
+            yield (new_name, data_torch)
+            return
+
+        yield from super().modify_tensors(data_torch, name, bid)
+
+    def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        yield from self._generate_code2wav_tensors()
+
+    def _generate_code2wav_tensors(self) -> Iterable[tuple[str, Tensor]]:
+        # code2wav weights live in speech_tokenizer/model.safetensors, not the main safetensors
+        from safetensors.torch import load_file
+
+        wav_config = self._wav_decoder_config()
+        state_dict = load_file(self.dir_model / "speech_tokenizer" / "model.safetensors")
+
+        def get(name: str) -> Tensor:
+            return state_dict[name]
+
+        def snake_fold(alpha: Tensor, beta: Tensor) -> tuple[Tensor, Tensor]:
+            # fold SnakeBeta's exp()/reciprocal here, so the graph is only mul/sin/sqr/mul/add
+            return torch.exp(alpha), 1.0 / (torch.exp(beta) + 1e-9)
+
+        def rvq_codebook(prefix: str, n_layers: int) -> Tensor:
+            # checkpoint has EMA accumulators, so codebook[i] = embedding_sum[i] / cluster_usage[i]
+            books = []
+            for i in range(n_layers):
+                embedding_sum = get(f"{prefix}.vq.layers.{i}._codebook.embedding_sum")
+                cluster_usage = get(f"{prefix}.vq.layers.{i}._codebook.cluster_usage")
+                books.append(embedding_sum / cluster_usage.clamp_min(1e-5).unsqueeze(-1))
+            return torch.stack(books, dim=0) if n_layers > 1 else books[0]
+
+        T = gguf.MODEL_TENSOR
+
+        # --- quantizer: RVQ codebook decode ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_FIRST_IN), get("decoder.quantizer.rvq_first.input_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_FIRST_OUT), get("decoder.quantizer.rvq_first.output_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_FIRST_CB), rvq_codebook("decoder.quantizer.rvq_first", 1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_REST_IN), get("decoder.quantizer.rvq_rest.input_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_REST_OUT), get("decoder.quantizer.rvq_rest.output_proj.weight").squeeze(-1))
+        yield (self.format_tensor_name(T.A_GEN_WAV_QUANT_REST_CB), rvq_codebook("decoder.quantizer.rvq_rest", self._CODE_GEN_N_CODEBOOKS))
+
+        # --- pre_conv ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_PRE_CONV, suffix=".weight"), get("decoder.pre_conv.conv.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_PRE_CONV, suffix=".bias"), get("decoder.pre_conv.conv.bias"))
+
+        # --- pre_transformer ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_IN_PROJ, suffix=".weight"), get("decoder.pre_transformer.input_proj.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_IN_PROJ, suffix=".bias"), get("decoder.pre_transformer.input_proj.bias"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_OUT_PROJ, suffix=".weight"), get("decoder.pre_transformer.output_proj.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_OUT_PROJ, suffix=".bias"), get("decoder.pre_transformer.output_proj.bias"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_TFM_OUTPUT_NORM), get("decoder.pre_transformer.norm.weight"))
+
+        tfm_layer_map = {
+            "input_layernorm.weight":         T.A_GEN_WAV_TFM_ATTN_NORM,
+            "self_attn.q_proj.weight":        T.A_GEN_WAV_TFM_ATTN_Q,
+            "self_attn.k_proj.weight":        T.A_GEN_WAV_TFM_ATTN_K,
+            "self_attn.v_proj.weight":        T.A_GEN_WAV_TFM_ATTN_V,
+            "self_attn.o_proj.weight":        T.A_GEN_WAV_TFM_ATTN_OUT,
+            "self_attn_layer_scale.scale":    T.A_GEN_WAV_TFM_ATTN_SCALE,
+            "post_attention_layernorm.weight": T.A_GEN_WAV_TFM_FFN_NORM,
+            "mlp.gate_proj.weight":           T.A_GEN_WAV_TFM_FFN_GATE,
+            "mlp.up_proj.weight":             T.A_GEN_WAV_TFM_FFN_UP,
+            "mlp.down_proj.weight":           T.A_GEN_WAV_TFM_FFN_DOWN,
+            "mlp_layer_scale.scale":          T.A_GEN_WAV_TFM_FFN_SCALE,
+        }
+        assert wav_config is not None
+        for bid in range(wav_config["num_hidden_layers"]):
+            for key, tensor_id in tfm_layer_map.items():
+                yield (self.format_tensor_name(tensor_id, bid), get(f"decoder.pre_transformer.layers.{bid}.{key}"))
+
+        # --- upsample: 2x (causal ConvTranspose1d + ConvNeXt block) ---
+        up_map = {
+            "0.conv.weight":     (T.A_GEN_WAV_UP_CONV, ".weight"),
+            "0.conv.bias":       (T.A_GEN_WAV_UP_CONV, ".bias"),
+            "1.dwconv.conv.weight": (T.A_GEN_WAV_UP_DWCONV, ".weight"),
+            "1.dwconv.conv.bias":   (T.A_GEN_WAV_UP_DWCONV, ".bias"),
+            "1.norm.weight":     (T.A_GEN_WAV_UP_NORM, ".weight"),
+            "1.norm.bias":       (T.A_GEN_WAV_UP_NORM, ".bias"),
+            "1.pwconv1.weight":  (T.A_GEN_WAV_UP_PW1, ".weight"),
+            "1.pwconv1.bias":    (T.A_GEN_WAV_UP_PW1, ".bias"),
+            "1.pwconv2.weight":  (T.A_GEN_WAV_UP_PW2, ".weight"),
+            "1.pwconv2.bias":    (T.A_GEN_WAV_UP_PW2, ".bias"),
+            "1.gamma":           (T.A_GEN_WAV_UP_GAMMA, ""),
+        }
+        for bid in range(len(wav_config["upsampling_ratios"])):
+            for key, (tensor_id, suffix) in up_map.items():
+                yield (self.format_tensor_name(tensor_id, bid, suffix=suffix), get(f"decoder.upsample.{bid}.{key}"))
+
+        # --- DAC decoder ---
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_ENTRY, suffix=".weight"), get("decoder.decoder.0.conv.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_ENTRY, suffix=".bias"), get("decoder.decoder.0.conv.bias"))
+
+        n_dac_blocks = len(wav_config["upsample_rates"])
+        for bid in range(n_dac_blocks):
+            py = bid + 1  # decoder.decoder.0 is the entry conv, blocks start at 1
+
+            a, b = snake_fold(get(f"decoder.decoder.{py}.block.0.alpha"), get(f"decoder.decoder.{py}.block.0.beta"))
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_SNAKE, bid, suffix=".alpha"), a)
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_SNAKE, bid, suffix=".beta"), b)
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_CONV, bid, suffix=".weight"), get(f"decoder.decoder.{py}.block.1.conv.weight"))
+            yield (self.format_tensor_name(T.A_GEN_WAV_DAC_UP_CONV, bid, suffix=".bias"), get(f"decoder.decoder.{py}.block.1.conv.bias"))
+
+            for xid in range(3):
+                ridx = xid + 2  # block.2/3/4 are the 3 residual units
+
+                a1, b1 = snake_fold(get(f"decoder.decoder.{py}.block.{ridx}.act1.alpha"), get(f"decoder.decoder.{py}.block.{ridx}.act1.beta"))
+                name1 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_ACT1].format(bid=bid, xid=xid)
+                yield (name1 + ".alpha", a1)
+                yield (name1 + ".beta", b1)
+
+                name_conv1 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_CONV1].format(bid=bid, xid=xid)
+                yield (name_conv1 + ".weight", get(f"decoder.decoder.{py}.block.{ridx}.conv1.conv.weight"))
+                yield (name_conv1 + ".bias", get(f"decoder.decoder.{py}.block.{ridx}.conv1.conv.bias"))
+
+                a2, b2 = snake_fold(get(f"decoder.decoder.{py}.block.{ridx}.act2.alpha"), get(f"decoder.decoder.{py}.block.{ridx}.act2.beta"))
+                name2 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_ACT2].format(bid=bid, xid=xid)
+                yield (name2 + ".alpha", a2)
+                yield (name2 + ".beta", b2)
+
+                name_conv2 = gguf.TENSOR_NAMES[T.A_GEN_WAV_DAC_RES_CONV2].format(bid=bid, xid=xid)
+                yield (name_conv2 + ".weight", get(f"decoder.decoder.{py}.block.{ridx}.conv2.conv.weight"))
+                yield (name_conv2 + ".bias", get(f"decoder.decoder.{py}.block.{ridx}.conv2.conv.bias"))
+
+        a5, b5 = snake_fold(get("decoder.decoder.5.alpha"), get("decoder.decoder.5.beta"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_SNAKE, suffix=".alpha"), a5)
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_SNAKE, suffix=".beta"), b5)
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_CONV, suffix=".weight"), get("decoder.decoder.6.conv.weight"))
+        yield (self.format_tensor_name(T.A_GEN_WAV_DAC_POST_CONV, suffix=".bias"), get("decoder.decoder.6.conv.bias"))

convert_hf_to_gguf.py

@@ -126,8 +126,12 @@ def parse_args() -> argparse.Namespace:
         help="Export only the multi-token prediction (MTP) head as a separate GGUF, suitable for use as a speculative draft. An 'mtp-' prefix will be added to the output file name.",
     )
     parser.add_argument(
-        "--no-mtp", action="store_true",
-        help="Exclude the multi-token prediction (MTP) head from the converted GGUF. Pair with --mtp on a second run to publish trunk and MTP as two files. Note: the split form duplicates embeddings, but even though the bundled default is more space-efficient overall, this allows differing quantization which may be more performant.",
+        "--no-nextn", "--no-mtp", dest="no_mtp", action="store_true",
+        help="Exclude NextN speculative draft tensors from the converted GGUF. Pair with --mtp or --dspark on a second run to publish target and draft as two files.",
+    )
+    parser.add_argument(
+        "--dspark", action="store_true",
+        help="Export only the DeepSeek-V4 DSpark draft tensors as a separate GGUF.",
     )
     parser.add_argument(
         "--mistral-format", action="store_true",
@@ -258,13 +262,20 @@ def main() -> None:
             from conversion.mistral import MistralModel
             model_class = MistralModel
 
-        if args.mtp and args.no_mtp:
-            logger.error("--mtp and --no-mtp are mutually exclusive")
+        if sum((args.mtp, args.no_mtp, args.dspark)) > 1:
+            logger.error("--mtp, --no-nextn, and --dspark are mutually exclusive")
             sys.exit(1)
 
+        if args.dspark:
+            if is_mistral_format or model_architecture != "DeepseekV4ForCausalLM":
+                logger.error("--dspark is only supported for DeepseekV4ForCausalLM")
+                sys.exit(1)
+            from conversion.deepseek import DeepseekV4DSparkModel
+            model_class = DeepseekV4DSparkModel
+
         if args.mtp or args.no_mtp:
             if not model_class.supports_mtp_export:
-                logger.error("--mtp / --no-mtp are not supported for %s", model_architecture)
+                logger.error("--mtp / --no-nextn are not supported for %s", model_architecture)
                 sys.exit(1)
             if args.no_mtp:
                 model_class.no_mtp = True

docs/release.md

@@ -0,0 +1,49 @@
+# Release process
+
+llama.cpp uses [semantic versioning](https://semver.org) (`MAJOR.MINOR.PATCH`).
+
+## Version bump guidelines
+
+| Change type | Version component |
+|---|---|
+| Breaking change to the public C API (`include/llama.h`)         | `MAJOR` |
+| Backward-compatible features, model support, or API addition    | `MINOR` |
+| Bug fix with no API change                                      | `PATCH` |
+
+The version is set in the three variables at the top of the root `CMakeLists.txt`:
+
+```cmake
+set(LLAMA_VERSION_MAJOR 0)
+set(LLAMA_VERSION_MINOR 1)
+set(LLAMA_VERSION_PATCH 0)
+```
+
+_A version bump should be included in the PR that introduces the change, or in a
+dedicated bump commit merged before the release is cut._
+
+_TODO: add PR labels (`semver: patch`, `semver: minor`, `semver: major`) to help
+identify which PRs require a version bump before cutting a release._
+
+## Making a release
+
+Releases are created by running the [make-release](.github/workflows/make-release.yml)
+which is a manual workflow.
+
+The workflow creates an annotated git tag (e.g. `v0.1.0`) and pushes it to the
+remote. No GitHub Release object is created, the tag is the release artifact.
+
+## Building a release
+
+By default, `LLAMA_BUILD_IS_DEV=ON` which appends a `-dev` suffix to `LLAMA_VERSION`,
+marking the build as a nightly/development build. Distributors building from a
+release tag must pass `-DLLAMA_BUILD_IS_DEV=OFF` to produce a clean version string
+(e.g. `0.1.0` instead of `0.1.0-dev`).
+
+## How releases reach users
+Currently releases are not published to github releases, only nightly/development
+builds are available there. The way users can access releases are using the following
+channels:
+
+- **llama-install.sh**  — downloads pre-built binaries built from the release tag.
+- **Package managers**  — consume the git tag directly.
+- **Build from source** — users clone the repo and check out the tag.

embd_res/klite.embd

@@ -12,7 +12,7 @@ Current version indicated by LITEVER below.
 -->
 <head>
 	<script id="init-config">
-	const LITEVER = 341;
+	const LITEVER = 342;
 	const urlParams = new URLSearchParams(window.location.search);
 	var localflag = urlParams.get('local'); //this will be replaced automatically in embedded kcpp
 	const STORAGE_PREFIX = (localflag?"e_":"")+"kaihordewebui_";
@@ -4876,6 +4876,15 @@ Current version indicated by LITEVER below.
 		"assistant_start":"[/INST]",
 		"assistant_end":"</s>",
 	},
+	{
+		"name":"Muse Glimmer",
+		"system_start": "<|start|>system<|message|>",
+		"system_end": "<|eot|>",
+		"user_start": "<|start|>user<|message|>",
+		"user_end": "<|eot|>",
+		"assistant_start": "<|start|>assistant to=user<|message|>",
+		"assistant_end": "<|eot|>"
+	},
 	{
 		"name":"Phi-3 Mini",
 		"system_start":"<|system|>\\n",
@@ -20982,7 +20991,7 @@ Current version indicated by LITEVER below.
 			let st = get_instruct_starttag(true);
 			let et = get_instruct_endtag(true);
 			let me = get_my_multiplayer_chatname();
-			seqs = [st, et];
+			seqs = [st]; //don't include the assistant tag, otherwise it breaks some think formats
 			if(localsettings.separate_end_tags)
 			{
 				if(get_instruct_endtag_end(true))
@@ -21004,7 +21013,7 @@ Current version indicated by LITEVER below.
 				//the model just gets too confused and is likely to repeat the tag.
 			}
 		}
-		//special case for GPT-OSS, never use it as a stop sequence or stuff gets messed
+		//special case for GPT-OSS and Muse Glimmer, never use it as a stop sequence or stuff gets messed
 		seqs = seqs.filter(itm => !itm.includes("<|start|>assistant"));
 		if(!localsettings.includedefaultstops)
 		{

examples/test-cmake/.gitignore

@@ -0,0 +1,3 @@
+llama-build-install
+install
+build

examples/test-cmake/CMakeLists.txt

@@ -0,0 +1,13 @@
+cmake_minimum_required(VERSION 3.14)
+project(llama-simple)
+
+set(CMAKE_CXX_STANDARD 17)
+
+find_package(llama 0.1.0 REQUIRED)
+
+add_executable(test-cmake test-cmake.cpp)
+target_link_libraries(test-cmake PRIVATE llama)
+target_compile_definitions(test-cmake PRIVATE
+    LLAMA_BUILD_NUMBER=${LLAMA_BUILD_NUMBER}
+    LLAMA_BUILD_COMMIT="${LLAMA_BUILD_COMMIT}"
+)

examples/test-cmake/README.md

@@ -0,0 +1,36 @@
+## cmake-test
+
+This is just for manually testing/developing of a llama.cpp installation to
+enable troubleshooting issues and exploration. The idea is that this can be used
+after making changes to llama.cpp installation cmake configuration and then
+verify it locally.
+
+### Usage
+The following will configure, build, and install llama.cpp
+
+Configuring/build/install:
+```console
+./build-install.sh
+```
+The above command will create a directory named `install` in the current directory
+which will have the follwing files in its lib directory:
+```console
+(venv) $ ls install/lib/
+cmake                   libggml.so          libllama-common.so.0      libllama.so.0.1.0  llama.cpp
+libggml-base.so         libggml.so.0        libllama-common.so.0.1.0  libmtmd.so         pkgconfig
+libggml-base.so.0       libggml.so.0.19.0   libllama.so               libmtmd.so.0
+libggml-base.so.0.19.0  libllama-common.so  libllama.so.0             libmtmd.so.0.1.0
+```
+
+Build/run this project using the installation created above:
+```console
+(venv) $ ./build.sh
+-- Configuring done (0.0s)
+-- Generating done (0.0s)
+-- Build files have been written to: /path/to/llama.cpp/examples/test-cmake/build
+[100%] Built target test-cmake
+[test-cmake] Using llama.cpp version 0.1.0-dev-b10335
+[test-cmake] Initializing backend...
+load_backend: loaded CPU backend from /path/to/llama.cpp/examples/test-cmake/install/lib/llama.cpp/libggml-cpu-alderlake.so
+[test-cmake] Backend initialized.
+```

examples/test-cmake/build-install.sh

@@ -0,0 +1,19 @@
+#!/bin/bash
+
+set -e
+
+rm -rf llama-build-install install
+
+cmake --fresh -S ../../. -B llama-build-install -DCMAKE_BUILD_TYPE=Release \
+  -DBUILD_SHARED_LIBS=ON \
+  -DGGML_BACKEND_DL=ON \
+  -DGGML_CPU_ALL_VARIANTS=ON \
+  -DLLAMA_TESTS_INSTALL=OFF \
+  -DCMAKE_INSTALL_PREFIX="${PWD}/install" \
+  -DGGML_BACKEND_DIR="${PWD}/install/lib/llama.cpp" \
+  -DGGML_LIB_INSTALL_DIR="${PWD}/install/lib/llama.cpp" \
+  -DLLAMA_LIB_INSTALL_DIR="${PWD}/install/lib/llama.cpp" \
+  -DLLAMA_TOOLS_INSTALL=OFF
+
+cmake --build llama-build-install --parallel 12
+cmake --install llama-build-install

examples/test-cmake/build.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+set -e
+
+cmake -S . -B build -DCMAKE_PREFIX_PATH="${PWD}/install"
+cmake --build build
+LD_LIBRARY_PATH="${PWD}/install/lib/llama.cpp:${PWD}/install/lib${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" ./build/test-cmake

examples/test-cmake/test-cmake.cpp

@@ -0,0 +1,12 @@
+#include "llama.h"
+#include <cstdio>
+
+int main(void) {
+    printf("[test-cmake] version: %s, build: %d (%s)\n",
+           llama_version(), LLAMA_BUILD_NUMBER, LLAMA_BUILD_COMMIT);
+    printf("[test-cmake] Initializing backend...\n");
+    llama_backend_init();
+    printf("[test-cmake] Backend initialized.\n");
+    llama_backend_free();
+    return 0;
+}

expose.cpp

@@ -217,6 +217,14 @@ extern "C"
     {
         sdtype_abort_generation();
     }
+    sd_info_outputs sd_get_ongoing_generation_info()
+    {
+        return sdtype_get_ongoing_generation_info();
+    }
+    void sd_request_ongoing_generation_preview()
+    {
+        sdtype_request_ongoing_generation_preview();
+    }
 
     bool whisper_load_model(const whisper_load_model_inputs inputs)
     {

ggml/include/ggml-backend.h

@@ -154,6 +154,8 @@ extern "C" {
         bool buffer_from_host_ptr;
         // event synchronization
         bool events;
+        // mmap is supported for loading
+        bool mmap_support;
     };
 
     // all the device properties

ggml/include/ggml.h

@@ -2477,7 +2477,8 @@ extern "C" {
             struct ggml_tensor  * A,
             struct ggml_tensor  * B,
             struct ggml_tensor  * C,
-            struct ggml_tensor  * ids);
+            struct ggml_tensor  * ids,
+            int64_t               K);
 
     // partition into non-overlapping windows with padding if needed
     // example:
@@ -2806,6 +2807,12 @@ extern "C" {
             struct ggml_cgraph * cgraph,
             struct ggml_tensor * tensor);
 
+    // add the tensor and its parents to the graph without marking them for compute
+    // the flag is set later, when the tensor is reached from a node that computes
+    GGML_API void ggml_build_forward_order(
+            struct ggml_cgraph * cgraph,
+            struct ggml_tensor * tensor);
+
     GGML_API void ggml_build_backward_expand(
         struct ggml_context *  ctx,        // context for gradient computation
         struct ggml_cgraph  *  cgraph,

ggml/src/ggml-backend-meta.cpp

@@ -132,6 +132,7 @@ static void ggml_backend_meta_device_get_props(ggml_backend_dev_t dev, ggml_back
         /* .host_buffer           = */ false, // Not implemented.
         /* .buffer_from_host_ptr  = */ false, // Not implemented.
         /* .events                = */ false, // Not implemented.
+        /* .mmap_support          = */ true,
     };
     for (ggml_backend_dev_t simple_dev : meta_dev_ctx->simple_devs) {
         ggml_backend_dev_props tmp_props;
@@ -140,6 +141,7 @@ static void ggml_backend_meta_device_get_props(ggml_backend_dev_t dev, ggml_back
         props->caps.host_buffer          = props->caps.host_buffer          && tmp_props.caps.host_buffer;
         props->caps.buffer_from_host_ptr = props->caps.buffer_from_host_ptr && tmp_props.caps.buffer_from_host_ptr;
         props->caps.events               = props->caps.events               && tmp_props.caps.events;
+        props->caps.mmap_support         = props->caps.mmap_support         && tmp_props.caps.mmap_support;
     }
 }
 

ggml/src/ggml-backend.cpp

@@ -766,8 +766,9 @@ struct ggml_backend_sched_split {
     int backend_id;
     int i_start;
     int i_end;
-    struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
+    struct ggml_tensor ** inputs;
     int n_inputs;
+    int inputs_capacity;
     // graph view of this split
     struct ggml_cgraph graph;
 };
@@ -806,8 +807,9 @@ struct ggml_backend_sched {
     int cur_copy;
     int next_copy;
     ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
-    struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
+    struct ggml_tensor ** graph_inputs;
     int n_graph_inputs;
+    int graph_inputs_capacity;
 
     struct ggml_context * ctx;
 
@@ -833,6 +835,36 @@ struct ggml_backend_sched {
 #define tensor_id_copy(id, backend_id, copy_id) sched->hv_tensor_copies[(id) * sched->n_backends * sched->n_copies + (backend_id) * sched->n_copies + (copy_id)]
 #define tensor_copy(tensor, backend_id, copy_id) tensor_id_copy(hash_id(tensor), backend_id, copy_id)
 
+static void ggml_backend_sched_split_inputs_grow(struct ggml_backend_sched_split * split) {
+    int new_cap = GGML_SCHED_MAX_SPLIT_INPUTS;
+    if (split->inputs_capacity > 0) {
+        new_cap = 2*split->inputs_capacity;
+        GGML_LOG_WARN("%s: increasing split inputs capacity from %d to %d\n", __func__, split->inputs_capacity, new_cap);
+    }
+    auto * pnew = (struct ggml_tensor **) realloc((void *) split->inputs, new_cap * sizeof(struct ggml_tensor *));
+    if (pnew == NULL) {
+        GGML_LOG_ERROR("%s: failed to allocate %zu bytes\n", __func__, new_cap * sizeof(struct ggml_tensor *));
+        GGML_ABORT("failed to grow split inputs container");
+    }
+    split->inputs = pnew;
+    split->inputs_capacity = new_cap;
+}
+
+static void ggml_backend_sched_graph_inputs_grow(ggml_backend_sched_t sched) {
+    int new_cap = GGML_SCHED_MAX_SPLIT_INPUTS;
+    if (sched->graph_inputs_capacity > 0) {
+        new_cap = 2*sched->graph_inputs_capacity;
+        GGML_LOG_WARN("%s: increasing graph inputs capacity from %d to %d\n", __func__, sched->graph_inputs_capacity, new_cap);
+    }
+    auto * pnew = (struct ggml_tensor **) realloc((void *) sched->graph_inputs, new_cap * sizeof(struct ggml_tensor *));
+    if (pnew == NULL) {
+        GGML_LOG_ERROR("%s: failed to allocate %zu bytes\n", __func__, new_cap * sizeof(struct ggml_tensor *));
+        GGML_ABORT("failed to grow graph inputs container");
+    }
+    sched->graph_inputs = pnew;
+    sched->graph_inputs_capacity = new_cap;
+}
+
 // returns the priority of the backend, lower id is higher priority
 static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
     for (int i = 0; i < sched->n_backends; i++) {
@@ -1304,7 +1336,7 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra
                     }
                     // check if the split has too many inputs
                     // FIXME: count the number of inputs instead of only checking when full
-                    if (split->n_inputs == GGML_SCHED_MAX_SPLIT_INPUTS) {
+                    if (split->n_inputs >= split->inputs_capacity) {
                         const size_t id = hash_id(src);
                         int src_backend_id = sched->hv_tensor_backend_ids[id];
                         bool supported = ggml_backend_sched_buffer_supported(sched, src, cur_backend_id);
@@ -1320,10 +1352,14 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra
                 split->i_end = i;
                 i_split++;
                 if (i_split >= sched->splits_capacity) {
+                    int old_cap = sched->splits_capacity;
                     sched->splits_capacity *= 2;
                     sched->splits = (ggml_backend_sched_split *)
                         realloc(sched->splits, sched->splits_capacity * sizeof(struct ggml_backend_sched_split));
                     GGML_ASSERT(sched->splits != NULL);
+                    for (int k = old_cap; k < sched->splits_capacity; k++) {
+                        memset(&sched->splits[k], 0, sizeof(struct ggml_backend_sched_split));
+                    }
                 }
                 split = &sched->splits[i_split];
                 split->backend_id = node_backend_id;
@@ -1360,7 +1396,9 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra
                             SET_CAUSE(tensor_copy, "4.cpy");
                         }
                         int n_graph_inputs = sched->n_graph_inputs++;
-                        GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
+                        if (n_graph_inputs >= sched->graph_inputs_capacity) {
+                            ggml_backend_sched_graph_inputs_grow(sched);
+                        }
                         sched->graph_inputs[n_graph_inputs] = src;
                     }
                 }
@@ -1380,7 +1418,9 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra
                             SET_CAUSE(tensor_copy, "4.cpy");
                         }
                         int n_inputs = split->n_inputs++;
-                        GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
+                        if (n_inputs >= split->inputs_capacity) {
+                            ggml_backend_sched_split_inputs_grow(split);
+                        }
                         split->inputs[n_inputs] = src;
                     }
                     node->src[j] = tensor_id_copy(src_id, cur_backend_id, sched->cur_copy);
@@ -1406,7 +1446,11 @@ void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgra
         sched->prev_leaf_backend_ids = tmp;
     }
 
-    int graph_size = std::max(graph->n_nodes, graph->n_leafs) + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS*2*sched->n_copies;
+    int total_inputs = sched->n_graph_inputs;
+    for (int i = 0; i < sched->n_splits; i++) {
+        total_inputs += sched->splits[i].n_inputs;
+    }
+    int graph_size = std::max(graph->n_nodes, graph->n_leafs) + total_inputs * 2 * sched->n_copies;
 
     // remember the actual graph_size for performing reallocation checks later [GGML_SCHED_DEBUG_REALLOC]
     sched->debug_prev_graph_size = sched->debug_graph_size;
@@ -1793,6 +1837,9 @@ ggml_backend_sched_t ggml_backend_sched_new(
     sched->splits = (ggml_backend_sched_split *) calloc(initial_splits_capacity, sizeof(sched->splits[0]));
     sched->splits_capacity = initial_splits_capacity;
 
+    sched->graph_inputs_capacity = GGML_SCHED_MAX_SPLIT_INPUTS;
+    sched->graph_inputs = (struct ggml_tensor **) calloc(sched->graph_inputs_capacity, sizeof(struct ggml_tensor *));
+
     for (int b = 0; b < n_backends; b++) {
         sched->backends[b] = backends[b];
         sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
@@ -1825,7 +1872,11 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
     ggml_gallocr_free(sched->galloc);
     ggml_free(sched->ctx);
     ggml_hash_set_free(&sched->hash_set);
+    for (int i = 0; i < sched->splits_capacity; i++) {
+        free(sched->splits[i].inputs);
+    }
     free(sched->splits);
+    free(sched->graph_inputs);
     free(sched->hv_tensor_backend_ids);
     free(sched->hv_tensor_copies);
     free(sched->node_backend_ids);

ggml/src/ggml-blas/ggml-blas.cpp

@@ -367,6 +367,7 @@ static void ggml_backend_blas_device_get_props(ggml_backend_dev_t dev, struct gg
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ true,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-cpu/arch/arm/cpu-feats.cpp

@@ -1,81 +1,19 @@
 #include "ggml-backend-impl.h"
+#include "ggml-feats.h"
 
-#if defined(__aarch64__)
-
-#if defined(__linux__)
-#include <sys/auxv.h>
-#elif defined(__APPLE__)
-#include <sys/sysctl.h>
-#endif
-
-#if !defined(HWCAP2_SVE2)
-#define HWCAP2_SVE2 (1 << 1)
-#endif
-
-#if !defined(HWCAP2_I8MM)
-#define HWCAP2_I8MM (1 << 13)
-#endif
-
-#if !defined(HWCAP2_SME)
-#define HWCAP2_SME (1 << 23)
-#endif
-
-struct aarch64_features {
-    // has_neon not needed, aarch64 has NEON guaranteed
-    bool has_dotprod     = false;
-    bool has_fp16_va     = false;
-    bool has_sve         = false;
-    bool has_sve2        = false;
-    bool has_i8mm        = false;
-    bool has_sme         = false;
-    bool has_sme2        = false;
-
-    aarch64_features() {
-#if defined(__linux__)
-        uint32_t hwcap = getauxval(AT_HWCAP);
-        uint32_t hwcap2 = getauxval(AT_HWCAP2);
-
-        has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
-        has_fp16_va = !!(hwcap & HWCAP_FPHP);
-        has_sve     = !!(hwcap & HWCAP_SVE);
-        has_sve2    = !!(hwcap2 & HWCAP2_SVE2);
-        has_i8mm    = !!(hwcap2 & HWCAP2_I8MM);
-        has_sme     = !!(hwcap2 & HWCAP2_SME);
-#elif defined(__APPLE__)
-        int oldp = 0;
-        size_t size = sizeof(oldp);
-
-        if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, NULL, 0) == 0) {
-            has_dotprod = static_cast<bool>(oldp);
-        }
-
-        if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, NULL, 0) == 0) {
-            has_i8mm = static_cast<bool>(oldp);
-        }
-
-        if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, NULL, 0) == 0) {
-            has_sme = static_cast<bool>(oldp);
-        }
-
-        if (sysctlbyname("hw.optional.arm.FEAT_SME2", &oldp, &size, NULL, 0) == 0) {
-            has_sme2 = static_cast<bool>(oldp);
-        }
-
-        // Apple apparently does not implement SVE yet
-#endif
-    }
-};
+#if defined(__aarch64__) || defined(_M_ARM64)
 
 static int ggml_backend_cpu_aarch64_score() {
     int score = 1;
-    aarch64_features af;
+    const ggml_feats_arch64_runtime_t af = ggml_feats_get_arch64_runtime();
+    GGML_UNUSED(af);
 
 #ifdef GGML_USE_DOTPROD
     if (!af.has_dotprod) { return 0; }
     score += 1<<1;
 #endif
 #ifdef GGML_USE_FP16_VECTOR_ARITHMETIC
-    if (!af.has_fp16_va) { return 0; }
+    if (!af.has_fp16) { return 0; }
     score += 1<<2;
 #endif
 #ifdef GGML_USE_SVE
@@ -100,4 +38,4 @@ static int ggml_backend_cpu_aarch64_score() {
 
 GGML_BACKEND_DL_SCORE_IMPL(ggml_backend_cpu_aarch64_score)
 
-# endif // defined(__aarch64__)
+# endif // defined(__aarch64__) || defined(_M_ARM64)

ggml/src/ggml-cpu/ggml-cpu.c

@@ -3443,7 +3443,7 @@ static bool ggml_thread_apply_priority(int32_t prio) {
     return true;
 }
 
-#elif defined(__gnu_linux__)
+#elif defined(__linux__)
 // TODO: this may not work on BSD, to be verified
 
 static bool ggml_thread_apply_affinity(const bool * mask) {
@@ -3630,6 +3630,11 @@ struct ggml_cplan ggml_graph_plan(
     n_threads = 1;
 #endif
 
+#if defined(__wasi__)
+    // WASI doesn't support parallelism yet
+    n_threads = 1;
+#endif
+
     size_t work_size = 0;
 
     struct ggml_cplan cplan;

ggml/src/ggml-cpu/ggml-cpu.cpp

@@ -397,6 +397,7 @@ static void ggml_backend_cpu_device_get_props(ggml_backend_dev_t dev, struct ggm
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ true,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 
@@ -471,6 +472,8 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
                 src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
         case GGML_OP_CONV_2D:
             return ggml_is_contiguous(op->src[0]);
+        case GGML_OP_SSM_SCAN:
+            return ggml_get_op_params_i32(op, 0) == 1 || op->src[3]->ne[0] == 1;
         default:
             return true;
     }

ggml/src/ggml-cpu/ops.cpp

@@ -8941,7 +8941,7 @@ static void ggml_compute_forward_flash_attn_ext_tiled(
             for (int tk = 0; tk < kv_tile; tk++) {
                 const char * v_data = (const char *)v->data + (ic + tk)*nbv1 + iv2*nbv2 + iv3*nbv3;
                 if (kv_type == GGML_TYPE_F16) {
-                    ggml_fp16_to_fp32_row((const ggml_fp16_t *)v_data, V32 + tk * DV, DV);
+                    ggml_cpu_fp16_to_fp32((const ggml_fp16_t *)v_data, V32 + tk * DV, DV);
                 } else {
                     memcpy(V32 + tk * DV, v_data, DV * sizeof(float));
                 }
@@ -9644,11 +9644,13 @@ static void ggml_compute_forward_ssm_scan_f32(
     const int64_t ng = src4->ne[1];
     const int64_t nt = src1->ne[2]; // number of tokens per sequence
     const int64_t ns = src1->ne[3]; // number of sequences in the batch
+    const int64_t K  = ggml_get_op_params_i32(dst, 0);
 
     // can't use ggml_nbytes because src1 is not necessarily contiguous
     const int64_t s_off = ggml_nelements(src1) * ggml_element_size(src1);
 
-    GGML_ASSERT(ggml_nelements(src1) + nc*nr*nh*ns == ggml_nelements(dst));
+    GGML_ASSERT(K >= 1);
+    GGML_ASSERT(ggml_nelements(src1) + K*nc*nr*nh*ns == ggml_nelements(dst));
     GGML_ASSERT(src0->nb[0] == sizeof(float));
     GGML_ASSERT(src1->nb[0] == sizeof(float));
     GGML_ASSERT(src2->nb[0] == sizeof(float));
@@ -9657,6 +9659,7 @@ static void ggml_compute_forward_ssm_scan_f32(
     GGML_ASSERT(src5->nb[0] == sizeof(float));
     GGML_ASSERT(src6->nb[0] == sizeof(int32_t));
     GGML_ASSERT(nh % ng == 0);
+    GGML_ASSERT(src3->ne[0] == 1 || K == 1);
 
     // heads per thread
     const int dh = (nh + nth - 1)/nth;
@@ -9831,6 +9834,13 @@ static void ggml_compute_forward_ssm_scan_f32(
                     }
                 }
             }
+            const int64_t slot = nt - 1 - i2;
+            if (K > 1 && slot > 0 && slot < K) {
+                float * s_snapshot = (float *) ((char *) dst->data + s_off + (slot*ns + i3)*(src0->nb[3]));
+                for (int h = ih0; h < ih1; ++h) {
+                    memcpy((char *) s_snapshot + h*src0->nb[2], (char *) s + h*src0->nb[2], src0->nb[2]);
+                }
+            }
             // use the output as the source when it's not the first token-wise iteration
             s0 = s;
         }

ggml/src/ggml-cpu/spacemit/ime.cpp

@@ -195,6 +195,7 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> class tensor_
                     case GGML_TYPE_Q4_K:
                     case GGML_TYPE_Q6_K:
                     case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q5_K:
                         //case GGML_TYPE_MXFP4:
@@ -214,6 +215,7 @@ template <typename BLOC_TYPE, int64_t INTER_SIZE, int64_t NB_COLS> class tensor_
                     case GGML_TYPE_Q4_K:
                     case GGML_TYPE_Q6_K:
                     case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q5_K:
                         //case GGML_TYPE_MXFP4:

ggml/src/ggml-cuda/common.cuh

@@ -634,7 +634,8 @@ template <typename T> struct block_reduce_policy<block_reduce_method::MAX, T> {
 };
 
 template <block_reduce_method reduce_method_t, const unsigned int block_size_template = 0, typename T>
-static __device__ T block_reduce(T val, T * shared_vals) {
+static __device__ T block_reduce(T val, [[maybe_unused]] T * shared_vals) {
+    // for multi-warp reductions, callers must not reuse shared_vals until all reads from this invocation have completed
     val                           = block_reduce_policy<reduce_method_t, T>::reduce(val);
     const unsigned int block_size = block_size_template == 0 ? blockDim.x : block_size_template;
     if (block_size > WARP_SIZE) {

ggml/src/ggml-cuda/cpy.cu

@@ -253,9 +253,9 @@ static void ggml_cpy_f32_q8_0_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     GGML_ASSERT(ne % QK8_0 == 0);
-    const int64_t num_blocks = ne / QK8_0;
+    const int64_t num_blocks = (ne/QK8_0 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
+    cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
@@ -264,9 +264,9 @@ static void ggml_cpy_q8_0_f32_cuda(
     const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
-    const int64_t num_blocks = ne;
+    const int64_t num_blocks = (ne/QK8_0 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_q_f32<cpy_blck_q8_0_f32, QK8_0><<<num_blocks, 1, 0, stream>>>
+    cpy_q_f32<cpy_blck_q8_0_f32, QK8_0><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
@@ -276,9 +276,9 @@ static void ggml_cpy_f32_q4_0_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     GGML_ASSERT(ne % QK4_0 == 0);
-    const int64_t num_blocks = ne / QK4_0;
+    const int64_t num_blocks = (ne/QK4_0 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
+    cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
@@ -289,9 +289,9 @@ static void ggml_cpy_q4_0_f32_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
-    const int64_t num_blocks = ne;
+    const int64_t num_blocks = (ne/QK4_0 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0><<<num_blocks, 1, 0, stream>>>(
+    cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
          ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -302,9 +302,9 @@ static void ggml_cpy_f32_q4_1_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     GGML_ASSERT(ne % QK4_1 == 0);
-    const int64_t num_blocks = ne / QK4_1;
+    const int64_t num_blocks = (ne/QK4_1 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
+    cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
@@ -315,9 +315,9 @@ static void ggml_cpy_q4_1_f32_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
-    const int64_t num_blocks = ne;
+    const int64_t num_blocks = (ne/QK4_1 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1><<<num_blocks, 1, 0, stream>>>(
+    cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
          ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -328,9 +328,9 @@ static void ggml_cpy_f32_q5_0_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     GGML_ASSERT(ne % QK5_0 == 0);
-    const int64_t num_blocks = ne / QK5_0;
+    const int64_t num_blocks = (ne/QK5_0 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
+    cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
@@ -341,9 +341,9 @@ static void ggml_cpy_q5_0_f32_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
-    const int64_t num_blocks = ne;
+    const int64_t num_blocks = (ne/QK5_0 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0><<<num_blocks, 1, 0, stream>>>(
+    cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
         ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -354,9 +354,9 @@ static void ggml_cpy_f32_q5_1_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     GGML_ASSERT(ne % QK5_1 == 0);
-    const int64_t num_blocks = ne / QK5_1;
+    const int64_t num_blocks = (ne/QK5_1 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
+    cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 
@@ -367,9 +367,9 @@ static void ggml_cpy_q5_1_f32_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
     const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
     cudaStream_t stream) {
-    const int64_t num_blocks = ne;
+    const int64_t num_blocks = (ne/QK5_1 + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1><<<num_blocks, 1, 0, stream>>>(
+    cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>(
         cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
         ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
@@ -380,9 +380,9 @@ static void ggml_cpy_f32_iq4_nl_cuda(
     const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
 
     GGML_ASSERT(ne % QK4_NL == 0);
-    const int64_t num_blocks = ne / QK4_NL;
+    const int64_t num_blocks = (ne/QK4_NL + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
     GGML_ASSERT(num_blocks <= INT_MAX);
-    cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
+    cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
         (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
 }
 

ggml/src/ggml-cuda/ggml-cuda.cu

@@ -1865,6 +1865,37 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
     ggml_cuda_mul_mat_cublas(ctx, src0, src1, dst);
 }
 
+// returns true when ggml_cuda_mul_mat_id takes the fallback path that requires stream synchronization
+// [TAG_MUL_MAT_ID_CUDA_GRAPHS]
+static bool ggml_cuda_mul_mat_id_needs_sync(const ggml_tensor * dst, const int cc) {
+    const ggml_tensor * src0 = dst->src[0];
+    const ggml_tensor * src1 = dst->src[1];
+
+    if (src1->type != GGML_TYPE_F32 || dst->type != GGML_TYPE_F32) {
+        return true;
+    }
+
+    if (dst->ne[2] <= MMVQ_MAX_BATCH_SIZE) {
+        if (ggml_is_quantized(src0->type)) {
+            if (dst->ne[2] <= get_mmvq_mmid_max_batch(src0->type, cc)) {
+                return false;
+            }
+        } else if (GGML_CUDA_CC_IS_AMD(cc)) {
+            return false;
+        }
+    }
+
+    if (ggml_cuda_should_use_mmq(src0->type, cc, src1->ne[2], /*n_experts=*/src0->ne[2])) {
+        return false;
+    }
+
+    if (ggml_cuda_should_use_mmf(src0->type, cc, WARP_SIZE, src0->ne, src0->nb, src1->ne[2], /*mul_mat_id=*/true)) {
+        return false;
+    }
+
+    return true;
+}
+
 static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const ggml_tensor * src0 = dst->src[0];
     const ggml_tensor * src1 = dst->src[1];
@@ -1907,7 +1938,7 @@ static void ggml_cuda_mul_mat_id(ggml_backend_cuda_context & ctx, ggml_tensor *
     }
 
     // note: this path should not be reached when recording CUDA graphs, because it requires stream synchronization
-    // TODO: add asserts to verify this. should work with CUDA, HIP, etc.
+    GGML_ASSERT(ggml_cuda_mul_mat_id_needs_sync(dst, cc));
     cudaStream_t stream = ctx.stream();
 
     GGML_ASSERT(nb12 % nb11 == 0);
@@ -2526,10 +2557,8 @@ static bool ggml_cuda_graph_check_compability(ggml_cgraph * cgraph) {
         // [TAG_MUL_MAT_ID_CUDA_GRAPHS]
         if (node->op == GGML_OP_MUL_MAT_ID) {
             const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
-            const int mmvq_mmid_max = get_mmvq_mmid_max_batch(node->src[0]->type, cc);
-            if (!ggml_is_quantized(node->src[0]->type) || node->ne[2] > mmvq_mmid_max) {
-                // under these conditions, the mul_mat_id operation will need to synchronize the stream, so we cannot use CUDA graphs
-                // TODO: figure out a way to enable for larger batch sizes, without hurting performance
+            if (ggml_cuda_mul_mat_id_needs_sync(node, cc)) {
+                // the mul_mat_id fallback path synchronizes the stream, so we cannot use CUDA graphs
                 // ref: https://github.com/ggml-org/llama.cpp/pull/18958
                 use_cuda_graph = false;
 #ifndef NDEBUG
@@ -2659,6 +2688,52 @@ static bool ggml_cuda_should_fuse_rope_set_rows(const ggml_tensor * rope,
     return true;
 }
 
+static bool ggml_cuda_should_fuse_rms_norm_mul_rope(const ggml_tensor * rms_norm,
+                                                    const ggml_tensor * mul,
+                                                    const ggml_tensor * rope) {
+    if (rms_norm->op != GGML_OP_RMS_NORM || mul->op != GGML_OP_MUL || rope->op != GGML_OP_ROPE) {
+        return false;
+    }
+
+    if (rms_norm->src[0]->type != GGML_TYPE_F32 || rms_norm->type != GGML_TYPE_F32 ||
+        mul->src[0]->type != GGML_TYPE_F32 || mul->src[1]->type != GGML_TYPE_F32 ||
+        mul->type != GGML_TYPE_F32 || rope->type != GGML_TYPE_F32) {
+        return false;
+    }
+
+    if (rope->src[0] != mul) {
+        return false;
+    }
+
+    //if rms norm is the B operand, then we don't handle broadcast
+    if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm)) {
+        return false;
+    }
+
+    if (!ggml_are_same_shape(rms_norm, mul)) {
+        return false;
+    }
+
+    //rms_norm kernel assumes contiguous rows
+    if (!ggml_is_contiguous_rows(rms_norm->src[0]) ||
+        !ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) {
+        return false;
+    }
+
+    // the fused kernel handles the norm/neox rope modes only
+    const int mode = ((const int32_t *) rope->op_params)[2];
+    if (mode != GGML_ROPE_TYPE_NORMAL && mode != GGML_ROPE_TYPE_NEOX) {
+        return false;
+    }
+
+    const int n_dims = ((const int32_t *) rope->op_params)[1];
+    if (n_dims % 2 != 0 || rope->src[0]->ne[0] % 2 != 0) {
+        return false;
+    }
+
+    return true;
+}
+
 // match gated_delta_net + the strided cpy that scatters its state snapshots into the cache
 // (slot i -> rollback group i, slot 0 newest), so the kernel can write them and skip the cpy.
 static int ggml_cuda_try_gdn_cache_fusion(
@@ -2988,6 +3063,36 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
         }
     }
 
+    std::initializer_list<enum ggml_op> rms_norm_mul_rope_ops          = { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE };
+    std::initializer_list<enum ggml_op> rms_norm_mul_rope_set_rows_ops = { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS };
+
+    if (is_equal(rms_norm_mul_rope_set_rows_ops, ops) && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 4 })) {
+        const ggml_tensor * rms_norm = cgraph->nodes[node_idx];
+        const ggml_tensor * mul      = cgraph->nodes[node_idx + 1];
+        const ggml_tensor * rope     = cgraph->nodes[node_idx + 2];
+        const ggml_tensor * view     = cgraph->nodes[node_idx + 3];
+        const ggml_tensor * set_rows = cgraph->nodes[node_idx + 4];
+
+        if (ggml_check_edges(cgraph, node_idx, {{1, 0, 0}, {2, 0, 1}, {3, 0, 2}, {4, 0, 3}}) &&
+            ggml_cuda_should_fuse_rms_norm_mul_rope(rms_norm, mul, rope) &&
+            ggml_cuda_should_fuse_rope_set_rows(rope, view, set_rows)) {
+            int out_nodes[] = { node_idx + 4 };
+            return ggml_cuda_check_fusion_memory_ranges(cgraph, node_idx, (int)ops.size(), out_nodes, 1);
+        }
+    }
+
+    if (is_equal(rms_norm_mul_rope_ops, ops) && ggml_can_fuse(cgraph, node_idx, ops)) {
+        const ggml_tensor * rms_norm = cgraph->nodes[node_idx];
+        const ggml_tensor * mul      = cgraph->nodes[node_idx + 1];
+        const ggml_tensor * rope     = cgraph->nodes[node_idx + 2];
+
+        if (ggml_cuda_should_fuse_rms_norm_mul_rope(rms_norm, mul, rope)) {
+            int out_nodes[] = { node_idx + 2 };
+            return ggml_cuda_check_fusion_memory_ranges(cgraph, node_idx, (int)ops.size(), out_nodes, 1);
+        }
+        return false;
+    }
+
     std::initializer_list<enum ggml_op> rope_set_rows_ops = { GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS };
 
     if (is_equal(rope_set_rows_ops, ops) && ggml_can_fuse_subgraph(cgraph, node_idx, ops, { node_idx + 2 })) {
@@ -2996,7 +3101,8 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph *                cgraph,
         const ggml_tensor * set_rows = cgraph->nodes[node_idx + 2];
 
         if (ggml_cuda_should_fuse_rope_set_rows(rope, view, set_rows)) {
-            return true;
+            int out_nodes[] = { node_idx + 2 };
+            return ggml_cuda_check_fusion_memory_ranges(cgraph, node_idx, (int)ops.size(), out_nodes, 1);
         }
     }
 
@@ -3848,6 +3954,16 @@ static int ggml_cuda_try_fuse(ggml_backend_cuda_context * cuda_ctx, ggml_cgraph
         return fused_node_count - 1;
     }
 
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE, GGML_OP_VIEW, GGML_OP_SET_ROWS }, {})) {
+        ggml_cuda_op_rms_norm_mul_rope_fused(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2], cgraph->nodes[i + 4]);
+        return 4;
+    }
+
+    if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ROPE }, {})) {
+        ggml_cuda_op_rms_norm_mul_rope_fused(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2], nullptr);
+        return 2;
+    }
+
     if (ggml_cuda_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL, GGML_OP_ADD }, {})) {
         ggml_cuda_op_rms_norm_fused_add(*cuda_ctx, node, cgraph->nodes[i + 1], cgraph->nodes[i + 2]);
         return 2;
@@ -4041,7 +4157,11 @@ static void ggml_cuda_graph_evaluate_and_capture(ggml_backend_cuda_context * cud
                     continue;
                 }
 #ifndef NDEBUG
-                assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device));
+                // On integrated GPUs (APUs, e.g. RDNA3.5) the scheduler may place a
+                // node's output on the host-visible buffer, which the compute path
+                // handles. Allow that here, mirroring the src-tensor check below.
+                assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) ||
+                       (integrated && ggml_backend_buft_is_cuda_host(node->buffer->buft)));
                 for (int j = 0; j < GGML_MAX_SRC; j++) {
                     if (node->src[j] != nullptr) {
                         assert(node->src[j]->buffer);
@@ -4723,6 +4843,7 @@ static void ggml_backend_cuda_device_get_props(ggml_backend_dev_t dev, ggml_back
         /* .host_buffer           = */ host_buffer,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ events,
+        /* .mmap_support          = */ props->type != GGML_BACKEND_DEVICE_TYPE_IGPU,
     };
 }
 
@@ -5081,11 +5202,17 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                    (op->src[1]->type == GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F16) &&
                    (op->type         == GGML_TYPE_F32 || op->type         == GGML_TYPE_F16);
         case GGML_OP_SSM_SCAN: {
+            const int32_t K = ggml_get_op_params_i32(op, 0);
+
             if (op->src[3]->ne[0] == 1) {
                 // Mamba2
                 // (kernel only supports (d_state == 128 || d_state == 256) && d_head % 16 == 0)
                 return (op->src[0]->ne[0] == 128 || op->src[0]->ne[0] == 256) && op->src[0]->ne[1] % 16 == 0;
             } else {
+                if (K > 1) {
+                    return false;
+                }
+
                 // Mamba
                 // (kernel only supports d_state == 16, d_head == 1, n_head % 128 == 0, n_group == 1)
                 return op->src[0]->ne[0] == 16 && op->src[0]->ne[1] == 1 && op->src[0]->ne[2] % 128 == 0 && op->src[4]->ne[1] == 1;
@@ -5107,7 +5234,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
             return max_bias == 0.0f;
         }
         case GGML_OP_ROLL:
-            if(op->src[0]->type == GGML_TYPE_F32) {
+            if(op->src[0]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0])) {
                 return true;
             }
             return false;
@@ -5218,6 +5345,7 @@ static bool ggml_backend_cuda_device_offload_op(ggml_backend_dev_t dev, const gg
 
 static ggml_backend_event_t ggml_backend_cuda_device_event_new(ggml_backend_dev_t dev) {
 #ifdef GGML_CUDA_NO_PEER_COPY
+    GGML_UNUSED(dev);
     return nullptr;
 #else
     ggml_backend_cuda_device_context * dev_ctx = (ggml_backend_cuda_device_context *)dev->context;

ggml/src/ggml-cuda/norm.cu

@@ -64,7 +64,7 @@ static __global__ void group_norm_f32(const float * x, float * dst, const int gr
         tmp += xi * xi;
     }
 
-    tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum);
+    tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum + 32);
 
     const float variance = tmp / group_size;
     const float scale = rsqrtf(variance + eps);
@@ -297,7 +297,7 @@ static void group_norm_f32_cuda(
         group_norm_f32<WARP_SIZE><<<num_groups, block_dims, 0, stream>>>(x, dst, group_size, ne_elements, eps);
     } else {
         const dim3 block_dims(1024, 1, 1);
-        group_norm_f32<1024><<<num_groups, block_dims, block_dims.x > WARP_SIZE ? 32 * sizeof(float): 0, stream>>>(x, dst, group_size, ne_elements, eps);
+        group_norm_f32<1024><<<num_groups, block_dims, block_dims.x > WARP_SIZE ? 2 * 32 * sizeof(float): 0, stream>>>(x, dst, group_size, ne_elements, eps);
     }
 }
 

ggml/src/ggml-cuda/quantize.cu

@@ -8,7 +8,6 @@ struct __builtin_align__(32) float8 {
     float x; float y; float z; float w;
     float p; float q; float r; float s;
 };
-#endif
 
 #if CUDART_VERSION >= 12080
 static __device__ __forceinline__ float nvfp4_native_scale_error(
@@ -49,6 +48,7 @@ static __device__ __forceinline__ float nvfp4_native_scale_error(
     return err;
 }
 #endif // CUDART_VERSION >= 12080
+#endif // defined(BLACKWELL_MMA_AVAILABLE)
 
 __launch_bounds__(CUDA_QUANTIZE_BLOCK_SIZE, 1)
 static __global__ void quantize_q8_1(

ggml/src/ggml-cuda/rope.cu

@@ -670,3 +670,238 @@ void ggml_cuda_op_rope_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
 void ggml_cuda_op_rope_fused(ggml_backend_cuda_context & ctx, ggml_tensor * rope, ggml_tensor * set_rows) {
     ggml_cuda_op_rope_impl<true>(ctx, rope, set_rows);
 }
+
+// fused RMS_NORM + MUL + ROPE (+ VIEW + SET_ROWS)
+// one block per row: block_reduce gives the norm scale, then each thread applies mul and rope to the elements it owns
+template <int block_size, bool has_ff, typename D>
+static __global__ void rms_norm_mul_rope_f32(
+        const float * x, D * dst, const int ncols,
+        const int64_t s01, const int64_t s02, const int64_t s03,
+        const int64_t s1, const int64_t s2, const int64_t s3,
+        const float eps,
+        const float * mul,
+        const int64_t mul_s01, const int64_t mul_s02, const int64_t mul_s03,
+        const uint3 mul_ncols_packed, const uint3 mul_nrows_packed,
+        const uint3 mul_nchannels_packed, const uint3 mul_nsamples_packed,
+        const int n_dims, const int32_t * pos,
+        const float freq_scale, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims, const float theta_scale,
+        const float * freq_factors,
+        const int64_t * row_indices, const int set_rows_stride,
+        const bool is_neox) {
+    ggml_cuda_pdl_lc();
+    const int row     = blockIdx.x;
+    const int channel = blockIdx.y;
+    const int sample  = blockIdx.z;
+    const int tid     = threadIdx.x;
+
+    x += sample*s03 + channel*s02 + row*s01;
+
+    const uint32_t mul_row     = fastmodulo(row,     mul_nrows_packed);
+    const uint32_t mul_channel = fastmodulo(channel, mul_nchannels_packed);
+    const uint32_t mul_sample  = fastmodulo(sample,  mul_nsamples_packed);
+    mul += mul_sample*mul_s03 + mul_channel*mul_s02 + mul_row*mul_s01;
+
+    float tmp = 0.0f;
+
+    ggml_cuda_pdl_sync();
+    for (int col = tid; col < ncols; col += block_size) {
+        const float xi = x[col];
+        tmp += xi * xi;
+    }
+
+    extern __shared__ float s_sum[];
+    tmp = block_reduce<block_reduce_method::SUM, block_size>(tmp, s_sum);
+
+    const float scale = rsqrtf(tmp/ncols + eps);
+
+    int64_t idst = sample*s3 + channel*s2 + row*s1;
+    if (set_rows_stride != 0) {
+        idst = row*s1 + row_indices[channel]*set_rows_stride;
+    }
+    dst += idst;
+
+    for (int i0 = 2*tid; i0 < ncols; i0 += 2*block_size) {
+        int ix0;
+        int ix1;
+        if (is_neox && i0 < n_dims) {
+            ix0 = i0/2;
+            ix1 = i0/2 + n_dims/2;
+        } else {
+            ix0 = i0 + 0;
+            ix1 = i0 + 1;
+        }
+
+        const float x0 = scale * x[ix0] * mul[fastmodulo(ix0, mul_ncols_packed)];
+        const float x1 = scale * x[ix1] * mul[fastmodulo(ix1, mul_ncols_packed)];
+
+        if (i0 >= n_dims) {
+            dst[ix0] = ggml_cuda_cast<D>(x0);
+            dst[ix1] = ggml_cuda_cast<D>(x1);
+            continue;
+        }
+
+        const float theta_base  = pos[channel]*powf(theta_scale, i0/2.0f);
+        const float freq_factor = has_ff ? freq_factors[i0/2] : 1.0f;
+
+        float cos_theta;
+        float sin_theta;
+        rope_yarn<true>(theta_base/freq_factor, freq_scale, corr_dims, i0, ext_factor, attn_factor, cos_theta, sin_theta);
+
+        dst[ix0] = ggml_cuda_cast<D>(x0*cos_theta - x1*sin_theta);
+        dst[ix1] = ggml_cuda_cast<D>(x0*sin_theta + x1*cos_theta);
+    }
+}
+
+template <typename D>
+static void rms_norm_mul_rope_cuda(
+        const float * x, D * dst,
+        const int ncols, const int nrows, const int nchannels, const int nsamples,
+        const int64_t s01, const int64_t s02, const int64_t s03,
+        const int64_t s1, const int64_t s2, const int64_t s3,
+        const float eps,
+        const float * mul,
+        const int64_t mul_s01, const int64_t mul_s02, const int64_t mul_s03,
+        const uint32_t mul_ncols, const uint32_t mul_nrows,
+        const uint32_t mul_nchannels, const uint32_t mul_nsamples,
+        const int n_dims, const int32_t * pos,
+        const float freq_scale, const float freq_base, const float ext_factor, const float attn_factor,
+        const rope_corr_dims corr_dims,
+        const float * freq_factors,
+        const int64_t * row_indices, const int set_rows_stride,
+        const bool is_neox, cudaStream_t stream) {
+    GGML_ASSERT(ncols % 2 == 0);
+
+    const dim3 blocks_num(nrows, nchannels, nsamples);
+
+    const float theta_scale = powf(freq_base, -2.0f/n_dims);
+
+    const uint3 mul_ncols_packed     = init_fastdiv_values(mul_ncols);
+    const uint3 mul_nrows_packed     = init_fastdiv_values(mul_nrows);
+    const uint3 mul_nchannels_packed = init_fastdiv_values(mul_nchannels);
+    const uint3 mul_nsamples_packed  = init_fastdiv_values(mul_nsamples);
+
+    if (ncols < 1024) {
+        const dim3 block_dims(256, 1, 1);
+        const ggml_cuda_kernel_launch_params launch_params = {blocks_num, block_dims, 32*sizeof(float), stream};
+        if (freq_factors == nullptr) {
+            ggml_cuda_kernel_launch(rms_norm_mul_rope_f32<256, false, D>, launch_params,
+                x, dst, ncols, s01, s02, s03, s1, s2, s3, eps, mul, mul_s01, mul_s02, mul_s03,
+                mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed,
+                n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+                freq_factors, row_indices, set_rows_stride, is_neox);
+        } else {
+            ggml_cuda_kernel_launch(rms_norm_mul_rope_f32<256, true, D>, launch_params,
+                x, dst, ncols, s01, s02, s03, s1, s2, s3, eps, mul, mul_s01, mul_s02, mul_s03,
+                mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed,
+                n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+                freq_factors, row_indices, set_rows_stride, is_neox);
+        }
+    } else {
+        const dim3 block_dims(1024, 1, 1);
+        const ggml_cuda_kernel_launch_params launch_params = {blocks_num, block_dims, 32*sizeof(float), stream};
+        if (freq_factors == nullptr) {
+            ggml_cuda_kernel_launch(rms_norm_mul_rope_f32<1024, false, D>, launch_params,
+                x, dst, ncols, s01, s02, s03, s1, s2, s3, eps, mul, mul_s01, mul_s02, mul_s03,
+                mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed,
+                n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+                freq_factors, row_indices, set_rows_stride, is_neox);
+        } else {
+            ggml_cuda_kernel_launch(rms_norm_mul_rope_f32<1024, true, D>, launch_params,
+                x, dst, ncols, s01, s02, s03, s1, s2, s3, eps, mul, mul_s01, mul_s02, mul_s03,
+                mul_ncols_packed, mul_nrows_packed, mul_nchannels_packed, mul_nsamples_packed,
+                n_dims, pos, freq_scale, ext_factor, attn_factor, corr_dims, theta_scale,
+                freq_factors, row_indices, set_rows_stride, is_neox);
+        }
+    }
+}
+
+void ggml_cuda_op_rms_norm_mul_rope_fused(ggml_backend_cuda_context & ctx,
+        ggml_tensor * rms_norm, ggml_tensor * mul, ggml_tensor * rope, ggml_tensor * set_rows) {
+    const ggml_tensor * x = rms_norm->src[0];
+    const ggml_tensor * mul_src = mul->src[0] == rms_norm ? mul->src[1] : mul->src[0];
+
+    float eps = 0.0f;
+    memcpy(&eps, rms_norm->op_params, sizeof(float));
+    GGML_ASSERT(eps >= 0.0f);
+
+    GGML_ASSERT(x->type == GGML_TYPE_F32);
+    GGML_ASSERT(mul_src->type == GGML_TYPE_F32);
+    GGML_ASSERT(rope->type == GGML_TYPE_F32);
+
+    void *          dst_d           = rope->data;
+    ggml_type       dst_type        = rope->type;
+    const int64_t * row_indices     = nullptr;
+    int             set_rows_stride = 0;
+
+    if (set_rows != nullptr) {
+        dst_d           = set_rows->data;
+        dst_type        = set_rows->type;
+        row_indices     = (const int64_t *) set_rows->src[1]->data;
+        set_rows_stride = set_rows->nb[1] / ggml_type_size(set_rows->type);
+    }
+
+    const int n_dims     = ((const int32_t *) rope->op_params)[1];
+    const int mode       = ((const int32_t *) rope->op_params)[2];
+    const int n_ctx_orig = ((const int32_t *) rope->op_params)[4];
+
+    float freq_base;
+    float freq_scale;
+    float ext_factor;
+    float attn_factor;
+    float beta_fast;
+    float beta_slow;
+
+    memcpy(&freq_base,   (const int32_t *) rope->op_params +  5, sizeof(float));
+    memcpy(&freq_scale,  (const int32_t *) rope->op_params +  6, sizeof(float));
+    memcpy(&ext_factor,  (const int32_t *) rope->op_params +  7, sizeof(float));
+    memcpy(&attn_factor, (const int32_t *) rope->op_params +  8, sizeof(float));
+    memcpy(&beta_fast,   (const int32_t *) rope->op_params +  9, sizeof(float));
+    memcpy(&beta_slow,   (const int32_t *) rope->op_params + 10, sizeof(float));
+
+    const bool is_neox = mode & GGML_ROPE_TYPE_NEOX;
+
+    const int32_t * pos = (const int32_t *) rope->src[1]->data;
+
+    const float * freq_factors = rope->src[2] != nullptr ? (const float *) rope->src[2]->data : nullptr;
+
+    rope_corr_dims corr_dims;
+    ggml_rope_yarn_corr_dims(n_dims, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims.v);
+
+    const size_t ts0 = ggml_type_size(x->type);
+    GGML_ASSERT(x->nb[0] == ts0);
+    const int64_t s01 = x->nb[1] / ts0;
+    const int64_t s02 = x->nb[2] / ts0;
+    const int64_t s03 = x->nb[3] / ts0;
+
+    const size_t ts_mul = ggml_type_size(mul_src->type);
+    GGML_ASSERT(mul_src->nb[0] == ts_mul);
+    const int64_t mul_s01 = mul_src->nb[1] / ts_mul;
+    const int64_t mul_s02 = mul_src->nb[2] / ts_mul;
+    const int64_t mul_s03 = mul_src->nb[3] / ts_mul;
+
+    const size_t ts_dst = ggml_type_size(rope->type);
+    const int64_t s1 = rope->nb[1] / ts_dst;
+    const int64_t s2 = rope->nb[2] / ts_dst;
+    const int64_t s3 = rope->nb[3] / ts_dst;
+
+    cudaStream_t stream = ctx.stream();
+
+    if (dst_type == GGML_TYPE_F32) {
+        rms_norm_mul_rope_cuda((const float *) x->data, (float *) dst_d,
+            x->ne[0], x->ne[1], x->ne[2], x->ne[3], s01, s02, s03, s1, s2, s3, eps,
+            (const float *) mul_src->data, mul_s01, mul_s02, mul_s03,
+            mul_src->ne[0], mul_src->ne[1], mul_src->ne[2], mul_src->ne[3],
+            n_dims, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+            freq_factors, row_indices, set_rows_stride, is_neox, stream);
+    } else if (dst_type == GGML_TYPE_F16) {
+        rms_norm_mul_rope_cuda((const float *) x->data, (half *) dst_d,
+            x->ne[0], x->ne[1], x->ne[2], x->ne[3], s01, s02, s03, s1, s2, s3, eps,
+            (const float *) mul_src->data, mul_s01, mul_s02, mul_s03,
+            mul_src->ne[0], mul_src->ne[1], mul_src->ne[2], mul_src->ne[3],
+            n_dims, pos, freq_scale, freq_base, ext_factor, attn_factor, corr_dims,
+            freq_factors, row_indices, set_rows_stride, is_neox, stream);
+    } else {
+        GGML_ABORT("fatal error");
+    }
+}

ggml/src/ggml-cuda/rope.cuh

@@ -7,3 +7,5 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 void ggml_cuda_op_rope_back(ggml_backend_cuda_context & ctx, ggml_tensor * dst);
 
 void ggml_cuda_op_rope_fused(ggml_backend_cuda_context & ctx, ggml_tensor * dst, ggml_tensor * set_rows);
+
+void ggml_cuda_op_rms_norm_mul_rope_fused(ggml_backend_cuda_context & ctx, ggml_tensor * rms_norm, ggml_tensor * mul, ggml_tensor * rope, ggml_tensor * set_rows);

ggml/src/ggml-cuda/softmax.cu

@@ -116,6 +116,11 @@ static __global__ void soft_max_f32(
         vals[col] = val;
     }
 
+    if (block_size > WARP_SIZE) {
+        // sync is needed as we reuse buf_iw across block_reduce invocations, see #26385
+        // for block_size <= WARP_SIZE, block_reduce does not access buf_iw
+        __syncthreads();
+    }
     // find the sum of exps in the block
     tmp = block_reduce<block_reduce_method::SUM, block_size_template>(tmp, buf_iw);
 
@@ -142,6 +147,8 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
                                                                 float * __restrict__ dst,
                                                                 float * __restrict__ tmp_maxs,
                                                                 float * __restrict__ tmp_sums,
+                                                                float * shared_vals_max,
+                                                                float * shared_vals_sum,
                                                                 const soft_max_params p) {
     namespace cg = cooperative_groups;
 
@@ -154,7 +161,6 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     float     local_vals[n_elem_per_thread] = { -INFINITY, -INFINITY, -INFINITY, -INFINITY };
     float     local_max                     = -INFINITY;
     const int step_size                     = gridDim.x * blockDim.x;
-    __shared__ float shared_vals[32];
 
     // Compute thread-local max
     for (int col = col_start; col < p.ncols;) {
@@ -171,7 +177,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     }
 
     // Compute CTA-level max
-    local_max = block_reduce<block_reduce_method::MAX>(local_max, shared_vals);
+    local_max = block_reduce<block_reduce_method::MAX>(local_max, shared_vals_max);
 
     // Store CTA-level max to GMEM
     if (tid == 0) {
@@ -186,7 +192,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     } else {
         local_max = -INFINITY;
     }
-    local_max = block_reduce<block_reduce_method::MAX>(local_max, shared_vals);
+    local_max = block_reduce<block_reduce_method::MAX>(local_max, shared_vals_max);
 
     // Compute softmax dividends, accumulate divisor
     float tmp_expf = 0.0f;
@@ -209,7 +215,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     }
 
     // Reduce divisor within CTA
-    tmp_expf = block_reduce<block_reduce_method::SUM>(tmp_expf, shared_vals);
+    tmp_expf = block_reduce<block_reduce_method::SUM>(tmp_expf, shared_vals_sum);
 
     // Store CTA-level sum to GMEM
     if (tid == 0) {
@@ -223,7 +229,7 @@ static __device__ void soft_max_f32_parallelize_cols_single_row(const float * __
     } else {
         tmp_expf = 0.0f;
     }
-    tmp_expf = block_reduce<block_reduce_method::SUM>(tmp_expf, shared_vals);
+    tmp_expf = block_reduce<block_reduce_method::SUM>(tmp_expf, shared_vals_sum);
 
     // Divide dividend by global sum + store data
     for (int col = col_start; col < p.ncols;) {
@@ -310,9 +316,11 @@ __launch_bounds__(8*WARP_SIZE, 1) static __global__ void soft_max_f32_paralleliz
 // https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/device-callable-apis.html#grid-synchronization
 // https://docs.nvidia.com/cuda/cuda-programming-guide/05-appendices/device-callable-apis.html#class-cluster-group
 {
+    __shared__ float shared_vals[2][32];
+
     for (int rowx = 0; rowx < p.ne01 * p.ne02 * p.ne03; rowx++) {
         soft_max_f32_parallelize_cols_single_row(x + int64_t(rowx) * p.ncols, dst + int64_t(rowx) * p.ncols, tmp_maxs,
-                                                 tmp_sums, p);
+                                                 tmp_sums, shared_vals[0], shared_vals[1], p);
     }
 }
 

ggml/src/ggml-cuda/ssm-scan.cu

@@ -149,7 +149,7 @@ __global__ void __launch_bounds__(d_state, 1)
         const int src0_nb2, const int src0_nb3, const int src1_nb2, const int src1_nb3,
         const int src2_nb1, const int src2_nb2, const int src3_nb1,
         const int src4_nb2, const int src4_nb3, const int src5_nb2, const int src5_nb3,
-        const int64_t s_off, const int64_t n_head, const int64_t d_head, const int64_t n_group, const int64_t n_tok) {
+        const int64_t s_off, const int64_t n_head, const int64_t d_head, const int64_t n_group, const int64_t n_tok, const int64_t K) {
     const float   * GGML_CUDA_RESTRICT src0 = src0_ptr;
     const float   * GGML_CUDA_RESTRICT src1 = src1_ptr;
     const float   * GGML_CUDA_RESTRICT src2 = src2_ptr;
@@ -217,6 +217,16 @@ __global__ void __launch_bounds__(d_state, 1)
         if (lane == 0) {
             y_warp[i * stride_y] = state_sum;
         }
+
+        // Slot 0 is the final state written below; slots 1..K-1 are rollback snapshots.
+        const int64_t slot = n_tok - 1 - i;
+        if (K > 1 && slot > 0 && slot < K) {
+            float * s_snapshot_warp = (float *) ((char *) dst + s_off + (slot * gridDim.y + seq_idx) * src0_nb3 + head_idx * src0_nb2 + head_off * d_state);
+#pragma unroll
+            for (int j = 0; j < c_factor; j++) {
+                s_snapshot_warp[WARP_SIZE * j + lane] = state[j];
+            }
+        }
     }
 
     // write back the state
@@ -232,7 +242,7 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
                               const int src2_nb2, const int src3_nb1, const int src4_nb2, const int src4_nb3, const int src5_nb2,
                               const int src5_nb3, const int64_t s_off, const int64_t d_state, const int64_t head_dim,
                               const int64_t n_head, const int64_t n_group, const int64_t n_tok, const int64_t n_seq,
-                              cudaStream_t stream) {
+                              const int64_t K, cudaStream_t stream) {
     // NOTE: if you change conditions here, be sure to update the corresponding supports_op condition!
     if (src3_nb1 == sizeof(float)) {
         // Mamba-2
@@ -245,7 +255,7 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
             ggml_cuda_kernel_launch(ssm_scan_f32_group<128/WARP_SIZE, 128>, launch_params,
                     src0, src1, src2, src3, src4, src5, src6, dst,
                     src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
-                    src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok);
+                    src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok, K);
         } else if (d_state == 256) { // Falcon-H1
             constexpr int threads   = 256;
             constexpr int num_warps = threads/WARP_SIZE;
@@ -255,12 +265,13 @@ static void ssm_scan_f32_cuda(const float * src0, const float * src1, const floa
             ggml_cuda_kernel_launch(ssm_scan_f32_group<256/WARP_SIZE, 256>, launch_params,
                     src0, src1, src2, src3, src4, src5, src6, dst,
                     src0_nb2, src0_nb3, src1_nb2, src1_nb3, src2_nb1, src2_nb2, src3_nb1,
-                    src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok);
+                    src4_nb2, src4_nb3, src5_nb2, src5_nb3, s_off, n_head, head_dim, n_group, n_tok, K);
         } else {
             GGML_ABORT("doesn't support d_state!=(128 or 256).");
         }
     } else {
         // Mamba-1
+        GGML_ASSERT(K == 1);
         constexpr int threads = 128;
         GGML_ASSERT(n_head % threads == 0);
         GGML_ASSERT(head_dim == 1);
@@ -769,10 +780,12 @@ void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const int64_t ng  = src4->ne[1];  // n_group
     const int64_t n_t = src1->ne[2];  // number of tokens per sequence
     const int64_t n_s = src1->ne[3];  // number of sequences in the batch
+    const int32_t K_param = ggml_get_op_params_i32(dst, 0);
+    const int64_t K = K_param > 0 ? K_param : 1;
 
     const int64_t s_off = ggml_nelements(src1) * sizeof(float);
 
-    GGML_ASSERT(ggml_nelements(src1) + nc*nr*nh*n_s == ggml_nelements(dst));
+    GGML_ASSERT(ggml_nelements(src1) + K*nc*nr*nh*n_s == ggml_nelements(dst));
     GGML_ASSERT(src0->nb[0] == sizeof(float));
     GGML_ASSERT(src1->nb[0] == sizeof(float));
     GGML_ASSERT(src2->nb[0] == sizeof(float));
@@ -780,6 +793,7 @@ void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     GGML_ASSERT(src4->nb[0] == sizeof(float));
     GGML_ASSERT(src5->nb[0] == sizeof(float));
     GGML_ASSERT(src6->nb[0] == sizeof(int32_t));
+    GGML_ASSERT(src3->ne[0] == 1 || K == 1);
 
     const float * src0_d = (const float *) src0->data;
     const float * src1_d = (const float *) src1->data;
@@ -814,6 +828,7 @@ void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const bool is_mamba2 = (src3->nb[1] == sizeof(float));
     const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc;
     const bool use_ssd = is_mamba2 && n_t > SSM_SSD_MIN_TOKENS
+                      && K == 1
                       && n_t <= SSM_SSD_MAX_TOKENS
                       && GGML_CUDA_CC_IS_NVIDIA(cc)
                       && cc >= GGML_CUDA_CC_TURING
@@ -841,5 +856,5 @@ void ggml_cuda_op_ssm_scan(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     ssm_scan_f32_cuda(src0_d, src1_d, src2_d, src3_d, src4_d, src5_d, src6_d, dst_d,
                       src0->nb[2], src0->nb[3], src1->nb[2], src1->nb[3], src2->nb[1], src2->nb[2],
                       src3->nb[1], src4->nb[2], src4->nb[3], src5->nb[2], src5->nb[3],
-                      s_off, nc, nr, nh, ng, n_t, n_s, stream);
+                      s_off, nc, nr, nh, ng, n_t, n_s, K, stream);
 }

ggml/src/ggml-cuda/wkv.cu

@@ -141,6 +141,57 @@ static __global__ void rwkv_wkv7_f32(const int B, const int T, const int C, cons
     }
 }
 
+template <int rows_per_block>
+static __global__ void __launch_bounds__(WARP_SIZE * rows_per_block, 2)
+rwkv_wkv7_f32_t1_warp_row(const int T, const int C, const int H, const float * r, const float * w, const float * k, const float * v, const float * a, const float * b, const float * s, float * dst) {
+    constexpr int head_size = CUDA_WKV_BLOCK_SIZE;
+    constexpr int half_head = head_size / 2;
+
+    const int lane = threadIdx.x;
+    const int row  = blockIdx.y * rows_per_block + threadIdx.y;
+    const int bid  = blockIdx.x;
+
+    const int batch_i = bid / H;
+    const int head_i  = bid % H;
+    const int state_size = C * head_size;
+    const int head_off = head_i * head_size;
+    const int t = batch_i * C + head_off + row;
+
+    __shared__ float _r[head_size], _w[head_size], _k[head_size], _a[head_size], _b[head_size];
+
+    if (threadIdx.y == 0) {
+        _r[lane] = r[batch_i * C + head_off + lane];
+        _w[lane] = w[batch_i * C + head_off + lane];
+        _k[lane] = k[batch_i * C + head_off + lane];
+        _a[lane] = a[batch_i * C + head_off + lane];
+        _b[lane] = b[batch_i * C + head_off + lane];
+
+        _r[lane + half_head] = r[batch_i * C + head_off + lane + half_head];
+        _w[lane + half_head] = w[batch_i * C + head_off + lane + half_head];
+        _k[lane + half_head] = k[batch_i * C + head_off + lane + half_head];
+        _a[lane + half_head] = a[batch_i * C + head_off + lane + half_head];
+        _b[lane + half_head] = b[batch_i * C + head_off + lane + half_head];
+    }
+    __syncthreads();
+
+    const int64_t state_base = batch_i * state_size + head_i * head_size * head_size + row * head_size;
+    const float s0 = s[state_base + lane];
+    const float s1 = s[state_base + lane + half_head];
+    const float sa = warp_reduce_sum(_a[lane] * s0 + _a[lane + half_head] * s1);
+
+    const float vt  = v[t];
+    const float st0 = s0 * _w[lane]             + _k[lane]             * vt + sa * _b[lane];
+    const float st1 = s1 * _w[lane + half_head] + _k[lane + half_head] * vt + sa * _b[lane + half_head];
+    const float y   = warp_reduce_sum(st0 * _r[lane] + st1 * _r[lane + half_head]);
+
+    dst[T * C + state_base + lane]             = st0;
+    dst[T * C + state_base + lane + half_head] = st1;
+
+    if (lane == 0) {
+        dst[t] = y;
+    }
+}
+
 void ggml_cuda_op_rwkv_wkv6(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
     const float * k_d  = (const float *)dst->src[0]->data;
     const float * v_d  = (const float *)dst->src[1]->data;
@@ -191,7 +242,10 @@ void ggml_cuda_op_rwkv_wkv7(ggml_backend_cuda_context & ctx, ggml_tensor * dst)
     GGML_ASSERT(C % H == 0);
     GGML_ASSERT(C / H == CUDA_WKV_BLOCK_SIZE || C / H == CUDA_WKV_BLOCK_SIZE * 2);
 
-    if (C / H == CUDA_WKV_BLOCK_SIZE) {
+    if (T / B == 1 && C / H == CUDA_WKV_BLOCK_SIZE) {
+        constexpr int rows_per_block = 4;
+        rwkv_wkv7_f32_t1_warp_row<rows_per_block><<<dim3(B * H, CUDA_WKV_BLOCK_SIZE / rows_per_block), dim3(WARP_SIZE, rows_per_block), 0, stream>>>(T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d);
+    } else if (C / H == CUDA_WKV_BLOCK_SIZE) {
         rwkv_wkv7_f32<CUDA_WKV_BLOCK_SIZE><<<B * H, C / H, 0, stream>>>(B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d);
     } else {
         rwkv_wkv7_f32<CUDA_WKV_BLOCK_SIZE * 2><<<B * H, C / H, 0, stream>>>(B, T, C, H, r_d, w_d, k_d, v_d, a_d, b_d, s_d, dst_d);

ggml/src/ggml-feats.h

@@ -0,0 +1,166 @@
+#pragma once
+
+#if defined(__aarch64__) || defined(_M_ARM64)
+
+#if defined(__linux__)
+#include <sys/auxv.h>
+#include <sys/prctl.h>
+
+#if !defined(HWCAP2_SVE2)
+#define HWCAP2_SVE2 (1ULL << 1)
+#endif
+
+#if !defined(HWCAP_FPHP)
+#define HWCAP_FPHP (1 << 9)
+#endif
+
+#if !defined(HWCAP_ASIMDHP)
+#define HWCAP_ASIMDHP (1 << 10)
+#endif
+
+#if !defined(HWCAP2_I8MM)
+#define HWCAP2_I8MM (1ULL << 13)
+#endif
+
+#if !defined(HWCAP_ASIMDDP)
+#define HWCAP_ASIMDDP (1 << 20)
+#endif
+
+#if !defined(HWCAP_SVE)
+#define HWCAP_SVE (1 << 22)
+#endif
+
+#if !defined(HWCAP2_SME)
+#define HWCAP2_SME (1ULL << 23)
+#endif
+
+#if !defined(HWCAP2_SME2)
+#define HWCAP2_SME2 (1ULL << 37)
+#endif
+
+#if !defined(PR_SVE_GET_VL)
+#define PR_SVE_GET_VL 51
+#endif
+
+#if !defined(PR_SVE_VL_LEN_MASK)
+#define PR_SVE_VL_LEN_MASK 0xffff
+#endif
+
+#elif defined(__APPLE__)
+#include <sys/sysctl.h>
+#elif defined(_WIN32)
+#include <windows.h>
+
+#if !defined(PF_ARM_V82_DP_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_V82_DP_INSTRUCTIONS_AVAILABLE 43
+#endif
+
+#if !defined(PF_ARM_SVE_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SVE_INSTRUCTIONS_AVAILABLE 46
+#endif
+
+#if !defined(PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE 47
+#endif
+
+#if !defined(PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE 66
+#endif
+
+#if !defined(PF_ARM_V82_FP16_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_V82_FP16_INSTRUCTIONS_AVAILABLE 67
+#endif
+
+#if !defined(PF_ARM_SME_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SME_INSTRUCTIONS_AVAILABLE 70
+#endif
+
+#if !defined(PF_ARM_SME2_INSTRUCTIONS_AVAILABLE)
+#define PF_ARM_SME2_INSTRUCTIONS_AVAILABLE 71
+#endif
+
+#endif
+
+typedef struct ggml_feats_arch64_runtime {
+    bool has_dotprod;
+    bool has_fp16;
+    bool has_sve;
+    bool has_sve2;
+    bool has_i8mm;
+    bool has_sme;
+    bool has_sme2;
+    int sve_cnt;
+} ggml_feats_arch64_runtime_t;
+
+static inline ggml_feats_arch64_runtime_t ggml_feats_get_arch64_runtime(void) {
+    ggml_feats_arch64_runtime_t runtime_feat = {};
+
+#if defined(__linux__)
+    const unsigned long hwcap  = getauxval(AT_HWCAP);
+    const unsigned long hwcap2 = getauxval(AT_HWCAP2);
+
+    runtime_feat.has_dotprod = !!(hwcap & HWCAP_ASIMDDP);
+    runtime_feat.has_fp16    = !!(hwcap & HWCAP_FPHP) && !!(hwcap & HWCAP_ASIMDHP);;
+    runtime_feat.has_sve     = !!(hwcap & HWCAP_SVE);
+    runtime_feat.has_sve2    = !!(hwcap2 & HWCAP2_SVE2);
+    runtime_feat.has_i8mm    = !!(hwcap2 & HWCAP2_I8MM);
+    runtime_feat.has_sme     = !!(hwcap2 & HWCAP2_SME);
+    runtime_feat.has_sme2    = !!(hwcap2 & HWCAP2_SME2);
+
+    if (runtime_feat.has_sve) {
+        const int vl = prctl(PR_SVE_GET_VL);
+        if (vl >= 0) {
+            runtime_feat.sve_cnt = vl & PR_SVE_VL_LEN_MASK;
+        }
+    }
+#elif defined(__APPLE__)
+    int oldp = 0;
+    size_t size = sizeof(oldp);
+
+    if (sysctlbyname("hw.optional.arm.FEAT_DotProd", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_dotprod = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_FP16", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_fp16 = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SVE", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sve = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SVE2", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sve2 = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_I8MM", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_i8mm = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SME", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sme = static_cast<bool>(oldp);
+    }
+
+    if (sysctlbyname("hw.optional.arm.FEAT_SME2", &oldp, &size, nullptr, 0) == 0) {
+        runtime_feat.has_sme2 = static_cast<bool>(oldp);
+    }
+
+    // Apple does not support userspace non-streaming SVE; keep SVE vector length unknown.
+    runtime_feat.sve_cnt = 0;
+#elif defined (_WIN32)
+    runtime_feat.has_dotprod = IsProcessorFeaturePresent(PF_ARM_V82_DP_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_fp16    = IsProcessorFeaturePresent(PF_ARM_V82_FP16_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sve     = IsProcessorFeaturePresent(PF_ARM_SVE_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sve2    = IsProcessorFeaturePresent(PF_ARM_SVE2_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_i8mm    = IsProcessorFeaturePresent(PF_ARM_V82_I8MM_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sme     = IsProcessorFeaturePresent(PF_ARM_SME_INSTRUCTIONS_AVAILABLE) != 0;
+    runtime_feat.has_sme2    = IsProcessorFeaturePresent(PF_ARM_SME2_INSTRUCTIONS_AVAILABLE) != 0;
+
+    // Windows exposes SVE feature presence, but not the runtime SVE vector length here.
+    runtime_feat.sve_cnt = 0;
+#endif
+
+    return runtime_feat;
+}
+
+#endif // defined(__aarch64__) || defined(_M_ARM64)

ggml/src/ggml-metal/ggml-metal-device.cpp

@@ -477,6 +477,41 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max(ggml_me
     return res;
 }
 
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_lightning_indexer(
+        ggml_metal_library_t lib,
+        const ggml_tensor * op) {
+    GGML_ASSERT(op->op == GGML_OP_LIGHTNING_INDEXER);
+
+    char name[256];
+
+    snprintf(name, 256, "kernel_lightning_indexer_%s", ggml_type_name(op->src[1]->type));
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        res = ggml_metal_library_compile_pipeline(lib, name, name, nullptr);
+    }
+
+    return res;
+}
+
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_dsv4_hc(ggml_metal_library_t lib, ggml_op op) {
+    const char * name = nullptr;
+
+    switch (op) {
+        case GGML_OP_DSV4_HC_COMB: name = "kernel_dsv4_hc_comb_f32"; break;
+        case GGML_OP_DSV4_HC_PRE:  name = "kernel_dsv4_hc_pre_f32";  break;
+        case GGML_OP_DSV4_HC_POST: name = "kernel_dsv4_hc_post_f32"; break;
+        default: GGML_ABORT("fatal error");
+    }
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        res = ggml_metal_library_compile_pipeline(lib, name, name, nullptr);
+    }
+
+    return res;
+}
+
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv(ggml_metal_library_t lib, const ggml_tensor * op) {
     GGML_ASSERT(op->src[0]->type == GGML_TYPE_F32);
     GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32);
@@ -918,6 +953,11 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv(ggml_meta
                 nr0 = N_R0_IQ4_XS;
                 smem = 32*sizeof(float);
             } break;
+        case GGML_TYPE_TQ2_0:
+            {
+                nsg = N_SG_TQ2_0;
+                nr0 = N_R0_TQ2_0;
+            } break;
         default:
             {
                 GGML_LOG_ERROR("Asserting on type %d\n", (int) tsrc0);
@@ -1147,6 +1187,11 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_mul_mv_id(ggml_m
                 nr0 = N_R0_IQ4_XS;
                 smem = 32*sizeof(float);
             } break;
+        case GGML_TYPE_TQ2_0:
+            {
+                nsg = N_SG_TQ2_0;
+                nr0 = N_R0_TQ2_0;
+            } break;
         default:
             {
                 GGML_LOG_ERROR("Asserting on type %d\n", (int)op->src[2]->type);
@@ -2117,6 +2162,23 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_opt_step_sgd(ggm
     return res;
 }
 
+ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_silu_back(ggml_metal_library_t lib, const ggml_tensor * op) {
+    assert(op->op == GGML_OP_SILU_BACK);
+
+    char base[256];
+    char name[256];
+
+    snprintf(base, 256, "kernel_silu_back_%s", ggml_type_name(op->src[0]->type));
+    snprintf(name, 256, "%s", base);
+
+    ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
+    if (!res.pipeline) {
+        res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
+    }
+
+    return res;
+}
+
 ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_memset(ggml_metal_library_t lib, const ggml_tensor *  op) {
     GGML_ASSERT(op->type == GGML_TYPE_I64);
 

ggml/src/ggml-metal/ggml-metal-device.h

@@ -117,6 +117,7 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_diag
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_repeat            (ggml_metal_library_t lib, enum ggml_type tsrc);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_concat            (ggml_metal_library_t lib, enum ggml_type tsrc);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_unary             (ggml_metal_library_t lib, const struct ggml_tensor * op);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_silu_back         (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_glu               (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum               (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_sum_rows          (ggml_metal_library_t lib, const struct ggml_tensor * op);
@@ -124,6 +125,8 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_bl
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_cumsum_add        (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_tri               (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_soft_max          (ggml_metal_library_t lib, const struct ggml_tensor * op);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_lightning_indexer (ggml_metal_library_t lib, const struct ggml_tensor * op);
+struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_dsv4_hc           (ggml_metal_library_t lib, enum ggml_op op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv          (ggml_metal_library_t lib, const struct ggml_tensor * op);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_conv_batched  (ggml_metal_library_t lib, const struct ggml_tensor * op, int ssm_conv_bs);
 struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_ssm_scan          (ggml_metal_library_t lib, const struct ggml_tensor * op);

ggml/src/ggml-metal/ggml-metal-device.m

@@ -2,6 +2,7 @@
 
 #import "ggml-impl.h"
 #import "ggml-backend-impl.h"
+#import "ggml-metal-impl.h"
 
 #include <Foundation/Foundation.h>
 
@@ -1143,6 +1144,14 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                 default:
                     return false;
             }
+        case GGML_OP_SILU_BACK:
+            return (op->src[0]->type == GGML_TYPE_F32) &&
+                (op->src[1]->type == GGML_TYPE_F32) &&
+                (op->type == GGML_TYPE_F32) &&
+                ggml_is_contiguous(op->src[0]) &&
+                ggml_is_contiguous(op->src[1]) &&
+                ggml_is_contiguous(op) &&
+                ggml_are_same_shape(op->src[0], op->src[1]);
         case GGML_OP_GLU:
             switch (ggml_get_glu_op(op)) {
                 case GGML_GLU_OP_REGLU:
@@ -1187,6 +1196,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_MUL:
         case GGML_OP_DIV:
         case GGML_OP_ADD_ID:
+            return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && (op->src[0]->type == op->src[1]->type);
         case GGML_OP_ACC:
             return ggml_is_contiguous_rows(op->src[0]) && ggml_is_contiguous_rows(op->src[1]) && op->src[0]->type == GGML_TYPE_F32;
         case GGML_OP_REPEAT:
@@ -1264,8 +1274,9 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
         case GGML_OP_ARGSORT:
         case GGML_OP_TOP_K:
         case GGML_OP_ARANGE:
-        case GGML_OP_ROLL:
             return true;
+        case GGML_OP_ROLL:
+            return ggml_is_contiguous(op->src[0]);
         case GGML_OP_FLASH_ATTN_EXT:
             // for new head sizes, add checks here
             if (op->src[0]->ne[0] != 32 &&
@@ -1305,9 +1316,76 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                     return false;
             }
             return has_simdgroup_mm; // TODO: over-restricted for vec-kernels
-        case GGML_OP_SSM_CONV:
+        case GGML_OP_LIGHTNING_INDEXER:
+            if (op->src[0]->ne[0] != OP_LIGHTNING_INDEXER_DK ||
+                op->src[0]->ne[1] != OP_LIGHTNING_INDEXER_NH) {
+                return false;
+            }
+            if (!has_simdgroup_mm ||
+                op->src[0]->type != GGML_TYPE_F32 ||
+                op->src[2]->type != GGML_TYPE_F32 ||
+                op->src[3]->type != GGML_TYPE_F16 ||
+                op->type         != GGML_TYPE_F32 ||
+                !ggml_is_contiguous_rows(op->src[0]) ||
+                !ggml_is_contiguous_rows(op->src[1]) ||
+                !ggml_is_contiguous_rows(op->src[2]) ||
+                !ggml_is_contiguous_rows(op->src[3])) {
+                return false;
+            }
+            switch (op->src[1]->type) {
+                case GGML_TYPE_F32:
+                case GGML_TYPE_F16:
+                case GGML_TYPE_Q4_0:
+                case GGML_TYPE_Q4_1:
+                case GGML_TYPE_Q5_0:
+                case GGML_TYPE_Q5_1:
+                case GGML_TYPE_Q8_0:
+                    return true;
+                case GGML_TYPE_BF16:
+                    return has_bfloat;
+                default:
+                    return false;
+            }
+        case GGML_OP_DSV4_HC_COMB:
+            return has_simdgroup_reduction &&
+                op->src[0]->type == GGML_TYPE_F32 &&
+                op->src[1]->type == GGML_TYPE_F32 &&
+                op->src[2]->type == GGML_TYPE_F32 &&
+                op->type         == GGML_TYPE_F32 &&
+                op->src[0]->ne[0] == 24 &&
+                op->src[1]->ne[0] >= 3 &&
+                op->src[2]->ne[0] == 24 &&
+                ggml_is_contiguous_rows(op->src[0]) &&
+                ggml_is_contiguous_rows(op->src[1]) &&
+                ggml_is_contiguous_rows(op->src[2]);
+        case GGML_OP_DSV4_HC_PRE:
+            return has_simdgroup_reduction &&
+                op->src[0]->type == GGML_TYPE_F32 &&
+                op->src[1]->type == GGML_TYPE_F32 &&
+                op->type         == GGML_TYPE_F32 &&
+                op->src[0]->ne[1] == 4 &&
+                op->src[1]->ne[0] == 4 &&
+                ggml_is_contiguous_rows(op->src[0]) &&
+                ggml_is_contiguous_rows(op->src[1]);
+        case GGML_OP_DSV4_HC_POST:
+            return has_simdgroup_reduction &&
+                op->src[0]->type == GGML_TYPE_F32 &&
+                op->src[1]->type == GGML_TYPE_F32 &&
+                op->src[2]->type == GGML_TYPE_F32 &&
+                op->src[3]->type == GGML_TYPE_F32 &&
+                op->type         == GGML_TYPE_F32 &&
+                op->src[1]->ne[1] == 4 &&
+                op->src[2]->ne[0] == 4 &&
+                op->src[3]->ne[0] == 4 &&
+                op->src[3]->ne[1] == 4 &&
+                ggml_is_contiguous_rows(op->src[0]) &&
+                ggml_is_contiguous_rows(op->src[1]) &&
+                ggml_is_contiguous_rows(op->src[2]) &&
+                ggml_is_contiguous_rows(op->src[3]);
         case GGML_OP_SSM_SCAN:
             return has_simdgroup_reduction;
+        case GGML_OP_SSM_CONV:
+            return has_simdgroup_reduction;
         case GGML_OP_RWKV_WKV6:
         case GGML_OP_RWKV_WKV7:
             return true;
@@ -1336,6 +1414,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                            case GGML_TYPE_Q5_0:
                            case GGML_TYPE_Q5_1:
                            case GGML_TYPE_IQ4_NL:
+                           case GGML_TYPE_TQ2_0:
                            case GGML_TYPE_I32:
                                 return true;
                            default:
@@ -1364,6 +1443,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_Q8_0:
+                    case GGML_TYPE_TQ2_0:
                         switch (op->type) {
                             case GGML_TYPE_F32:
                             case GGML_TYPE_F16:
@@ -1399,6 +1479,7 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
                     case GGML_TYPE_Q5_0:
                     case GGML_TYPE_Q5_1:
                     case GGML_TYPE_IQ4_NL:
+                    case GGML_TYPE_TQ2_0:
                         return true;
                     default:
                         return false;

ggml/src/ggml-metal/ggml-metal-impl.h

@@ -87,6 +87,9 @@
 #define N_R0_IQ4_XS 2
 #define N_SG_IQ4_XS 2
 
+#define N_R0_TQ2_0 4
+#define N_SG_TQ2_0 2
+
 // function constants offsets
 #define FC_FLASH_ATTN_EXT_PAD          100
 #define FC_FLASH_ATTN_EXT_BLK          200
@@ -112,6 +115,13 @@
 #define OP_FLASH_ATTN_EXT_VEC_NQPSG 1
 #define OP_FLASH_ATTN_EXT_VEC_NCPSG 32
 
+#define OP_LIGHTNING_INDEXER_DK    128
+#define OP_LIGHTNING_INDEXER_NH     64
+#define OP_LIGHTNING_INDEXER_NHPTG   8
+#define OP_LIGHTNING_INDEXER_NKPSG   8
+#define OP_LIGHTNING_INDEXER_NSG     8
+#define OP_LIGHTNING_INDEXER_NBPTG   8
+
 #define OP_UNARY_NUM_SCALE      10
 #define OP_UNARY_NUM_FILL       11
 #define OP_UNARY_NUM_CLAMP      12
@@ -870,6 +880,7 @@ typedef struct {
     int64_t  n_group;
     int64_t  n_seq_tokens;
     int64_t  n_seqs;
+    int64_t  K;
     uint64_t s_off;
     uint64_t nb00;
     uint64_t nb01;
@@ -1171,6 +1182,66 @@ typedef struct {
     int64_t val;
 } ggml_metal_kargs_memset;
 
+typedef struct {
+    int32_t  n_kv;
+    int32_t  n_batch;
+    int32_t  mask_ne3;
+    uint64_t nb1;
+    uint64_t nb3;
+    uint64_t nbq1;
+    uint64_t nbq2;
+    uint64_t nbq3;
+    uint64_t nbk2;
+    uint64_t nbk3;
+    uint64_t nbw1;
+    uint64_t nbw3;
+    uint64_t nbm1;
+    uint64_t nbm3;
+} ggml_metal_kargs_lightning_indexer;
+
+typedef struct {
+    int32_t  n_tokens;
+    int32_t  n_iter;
+    uint64_t nb_m0;
+    uint64_t nb_m1;
+    uint64_t nb_s0;
+    uint64_t nb_b0;
+    uint64_t nb_d0;
+    uint64_t nb_d1;
+    uint64_t nb_d2;
+    float    eps;
+} ggml_metal_kargs_dsv4_hc_comb;
+
+typedef struct {
+    int32_t  n_embd;
+    int32_t  n_tokens;
+    uint64_t nb_x0;
+    uint64_t nb_x1;
+    uint64_t nb_x2;
+    uint64_t nb_w0;
+    uint64_t nb_w1;
+    uint64_t nb_d0;
+    uint64_t nb_d1;
+} ggml_metal_kargs_dsv4_hc_pre;
+
+typedef struct {
+    int32_t  n_embd;
+    int32_t  n_tokens;
+    uint64_t nb_x0;
+    uint64_t nb_x1;
+    uint64_t nb_r0;
+    uint64_t nb_r1;
+    uint64_t nb_r2;
+    uint64_t nb_p0;
+    uint64_t nb_p1;
+    uint64_t nb_c0;
+    uint64_t nb_c1;
+    uint64_t nb_c2;
+    uint64_t nb_d0;
+    uint64_t nb_d1;
+    uint64_t nb_d2;
+} ggml_metal_kargs_dsv4_hc_post;
+
 typedef struct {
     int32_t  ne00;
     int32_t  ne01;
@@ -1222,4 +1293,8 @@ typedef struct {
     int64_t  np;
 } ggml_metal_kargs_opt_step_sgd;
 
+typedef struct {
+    int64_t ne;
+} ggml_metal_kargs_silu_back;
+
 #endif // GGML_METAL_IMPL

ggml/src/ggml-metal/ggml-metal-ops.cpp

@@ -299,6 +299,10 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_unary(ctx, idx);
             } break;
+        case GGML_OP_SILU_BACK:
+            {
+                n_fuse = ggml_metal_op_silu_back(ctx, idx);
+            } break;
         case GGML_OP_GLU:
             {
                 n_fuse = ggml_metal_op_glu(ctx, idx);
@@ -316,6 +320,16 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
             {
                 n_fuse = ggml_metal_op_cumsum(ctx, idx);
             } break;
+        case GGML_OP_LIGHTNING_INDEXER:
+            {
+                n_fuse = ggml_metal_op_lightning_indexer(ctx, idx);
+            } break;
+        case GGML_OP_DSV4_HC_COMB:
+        case GGML_OP_DSV4_HC_PRE:
+        case GGML_OP_DSV4_HC_POST:
+            {
+                n_fuse = ggml_metal_op_dsv4_hc(ctx, idx);
+            } break;
         case GGML_OP_SOFT_MAX:
             {
                 n_fuse = ggml_metal_op_soft_max(ctx, idx);
@@ -1297,6 +1311,203 @@ int ggml_metal_op_diag(ggml_metal_op_t ctx, int idx) {
     return 1;
 }
 
+int ggml_metal_op_lightning_indexer(ggml_metal_op_t ctx, int idx) {
+    ggml_tensor * op = ctx->node(idx);
+
+    ggml_metal_encoder_t enc = ctx->enc;
+
+    GGML_ASSERT(op->op == GGML_OP_LIGHTNING_INDEXER);
+
+    const ggml_tensor * q = op->src[0];
+    const ggml_tensor * k = op->src[1];
+    const ggml_tensor * w = op->src[2];
+    const ggml_tensor * m = op->src[3];
+
+    GGML_ASSERT(q->type == GGML_TYPE_F32);
+    GGML_ASSERT(k->type == GGML_TYPE_F32  ||
+                k->type == GGML_TYPE_F16  ||
+                k->type == GGML_TYPE_BF16 ||
+                k->type == GGML_TYPE_Q4_0 ||
+                k->type == GGML_TYPE_Q4_1 ||
+                k->type == GGML_TYPE_Q5_0 ||
+                k->type == GGML_TYPE_Q5_1 ||
+                k->type == GGML_TYPE_Q8_0);
+    GGML_ASSERT(w->type == GGML_TYPE_F32);
+    GGML_ASSERT(m->type == GGML_TYPE_F16);
+    GGML_ASSERT(op->type == GGML_TYPE_F32);
+
+    GGML_ASSERT(q->ne[0] == OP_LIGHTNING_INDEXER_DK);
+    GGML_ASSERT(q->ne[1] == OP_LIGHTNING_INDEXER_NH);
+
+    ggml_metal_kargs_lightning_indexer args = {
+        /*.n_kv      =*/ (int32_t) k->ne[2],
+        /*.n_batch   =*/ (int32_t) q->ne[2],
+        /*.mask_ne3  =*/ (int32_t) m->ne[3],
+        /*.nb1       =*/ op->nb[1],
+        /*.nb3       =*/ op->nb[3],
+        /*.nbq1      =*/ q->nb[1],
+        /*.nbq2      =*/ q->nb[2],
+        /*.nbq3      =*/ q->nb[3],
+        /*.nbk2      =*/ k->nb[2],
+        /*.nbk3      =*/ k->nb[3],
+        /*.nbw1      =*/ w->nb[1],
+        /*.nbw3      =*/ w->nb[3],
+        /*.nbm1      =*/ m->nb[1],
+        /*.nbm3      =*/ m->nb[3],
+    };
+
+    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(q),  1);
+    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(k),  2);
+    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(w),  3);
+    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(m),  4);
+    ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 5);
+
+    const int nsg   = OP_LIGHTNING_INDEXER_NSG;
+    const int nkptg = OP_LIGHTNING_INDEXER_NKPSG*nsg;
+    const int nbptg = OP_LIGHTNING_INDEXER_NBPTG;
+
+    auto pipeline = ggml_metal_library_get_pipeline_lightning_indexer(ctx->lib, op);
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+    ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
+    ggml_metal_encoder_dispatch_threadgroups(enc,
+            (k->ne[2] + nkptg - 1)/nkptg,
+            (q->ne[2] + nbptg - 1)/nbptg,
+            q->ne[3], 32, nsg, 1);
+
+    return 1;
+}
+
+int ggml_metal_op_dsv4_hc(ggml_metal_op_t ctx, int idx) {
+    ggml_tensor * op = ctx->node(idx);
+
+    ggml_metal_encoder_t enc = ctx->enc;
+    auto pipeline = ggml_metal_library_get_pipeline_dsv4_hc(ctx->lib, op->op);
+
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+
+    switch (op->op) {
+        case GGML_OP_DSV4_HC_COMB:
+            {
+                const ggml_tensor * mixes = op->src[0];
+                const ggml_tensor * scale = op->src[1];
+                const ggml_tensor * base  = op->src[2];
+
+                GGML_ASSERT(mixes->type == GGML_TYPE_F32);
+                GGML_ASSERT(scale->type == GGML_TYPE_F32);
+                GGML_ASSERT(base->type  == GGML_TYPE_F32);
+                GGML_ASSERT(op->type    == GGML_TYPE_F32);
+                GGML_ASSERT(mixes->ne[0] == 24);
+                GGML_ASSERT(op->ne[0] == 4 && op->ne[1] == 4);
+
+                ggml_metal_kargs_dsv4_hc_comb args = {
+                    /*.n_tokens =*/ (int32_t) mixes->ne[1],
+                    /*.n_iter   =*/ ggml_get_op_params_i32(op, 1),
+                    /*.nb_m0    =*/ mixes->nb[0],
+                    /*.nb_m1    =*/ mixes->nb[1],
+                    /*.nb_s0    =*/ scale->nb[0],
+                    /*.nb_b0    =*/ base->nb[0],
+                    /*.nb_d0    =*/ op->nb[0],
+                    /*.nb_d1    =*/ op->nb[1],
+                    /*.nb_d2    =*/ op->nb[2],
+                    /*.eps      =*/ ggml_get_op_params_f32(op, 0),
+                };
+
+                ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(mixes), 1);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(scale), 2);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(base),  3);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op),    4);
+
+                // One SIMDgroup owns one 4x4 Sinkhorn matrix. Packing up to four
+                // independent tokens per threadgroup keeps both decode and prompt
+                // dispatches compact without any threadgroup-memory synchronization.
+                const int nsg = std::min(4, args.n_tokens);
+                ggml_metal_encoder_dispatch_threadgroups(
+                        enc, (args.n_tokens + nsg - 1)/nsg, 1, 1, 32, nsg, 1);
+            } break;
+        case GGML_OP_DSV4_HC_PRE:
+            {
+                const ggml_tensor * x       = op->src[0];
+                const ggml_tensor * weights = op->src[1];
+
+                GGML_ASSERT(x->type       == GGML_TYPE_F32);
+                GGML_ASSERT(weights->type == GGML_TYPE_F32);
+                GGML_ASSERT(op->type      == GGML_TYPE_F32);
+                GGML_ASSERT(x->ne[1] == 4);
+
+                ggml_metal_kargs_dsv4_hc_pre args = {
+                    /*.n_embd   =*/ (int32_t) x->ne[0],
+                    /*.n_tokens =*/ (int32_t) x->ne[2],
+                    /*.nb_x0    =*/ x->nb[0],
+                    /*.nb_x1    =*/ x->nb[1],
+                    /*.nb_x2    =*/ x->nb[2],
+                    /*.nb_w0    =*/ weights->nb[0],
+                    /*.nb_w1    =*/ weights->nb[1],
+                    /*.nb_d0    =*/ op->nb[0],
+                    /*.nb_d1    =*/ op->nb[1],
+                };
+
+                ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(x),       1);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(weights), 2);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op),      3);
+
+                const int n_tiles = (args.n_embd + 31)/32;
+                const int nsg = std::min(4, n_tiles);
+                ggml_metal_encoder_dispatch_threadgroups(
+                        enc, (n_tiles + nsg - 1)/nsg, args.n_tokens, 1, 32, nsg, 1);
+            } break;
+        case GGML_OP_DSV4_HC_POST:
+            {
+                const ggml_tensor * x        = op->src[0];
+                const ggml_tensor * residual = op->src[1];
+                const ggml_tensor * post     = op->src[2];
+                const ggml_tensor * comb     = op->src[3];
+
+                GGML_ASSERT(x->type        == GGML_TYPE_F32);
+                GGML_ASSERT(residual->type == GGML_TYPE_F32);
+                GGML_ASSERT(post->type     == GGML_TYPE_F32);
+                GGML_ASSERT(comb->type     == GGML_TYPE_F32);
+                GGML_ASSERT(op->type       == GGML_TYPE_F32);
+                GGML_ASSERT(residual->ne[1] == 4);
+
+                ggml_metal_kargs_dsv4_hc_post args = {
+                    /*.n_embd   =*/ (int32_t) x->ne[0],
+                    /*.n_tokens =*/ (int32_t) x->ne[1],
+                    /*.nb_x0    =*/ x->nb[0],
+                    /*.nb_x1    =*/ x->nb[1],
+                    /*.nb_r0    =*/ residual->nb[0],
+                    /*.nb_r1    =*/ residual->nb[1],
+                    /*.nb_r2    =*/ residual->nb[2],
+                    /*.nb_p0    =*/ post->nb[0],
+                    /*.nb_p1    =*/ post->nb[1],
+                    /*.nb_c0    =*/ comb->nb[0],
+                    /*.nb_c1    =*/ comb->nb[1],
+                    /*.nb_c2    =*/ comb->nb[2],
+                    /*.nb_d0    =*/ op->nb[0],
+                    /*.nb_d1    =*/ op->nb[1],
+                    /*.nb_d2    =*/ op->nb[2],
+                };
+
+                ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(x),        1);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(residual), 2);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(post),     3);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(comb),     4);
+                ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op),       5);
+
+                const int n_tiles = (args.n_embd + 31)/32;
+                const int nsg = std::min(4, n_tiles);
+                ggml_metal_encoder_dispatch_threadgroups(
+                        enc, (n_tiles + nsg - 1)/nsg, args.n_tokens, 1, 32, nsg, 1);
+            } break;
+        default:
+            GGML_ABORT("fatal error");
+    }
+
+    return 1;
+}
+
 int ggml_metal_op_soft_max(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
@@ -1499,6 +1710,10 @@ int ggml_metal_op_ssm_scan(ggml_metal_op_t ctx, int idx) {
     const int64_t n_group      = ne41;
     const int64_t n_seq_tokens = ne12;
     const int64_t n_seqs       = ne13;
+    const int64_t K            = ggml_get_op_params_i32(op, 0);
+
+    GGML_ASSERT(K >= 1);
+    GGML_ASSERT(ggml_nelements(op->src[1]) + K*d_state*d_inner*n_head*n_seqs == ggml_nelements(op));
 
     ggml_metal_kargs_ssm_scan args = {
         /*.d_state      =*/ d_state,
@@ -1507,6 +1722,7 @@ int ggml_metal_op_ssm_scan(ggml_metal_op_t ctx, int idx) {
         /*.n_group      =*/ n_group,
         /*.n_seq_tokens =*/ n_seq_tokens,
         /*.n_seqs       =*/ n_seqs,
+        /*.K            =*/ K,
         /*.s_off        =*/ ggml_nelements(op->src[1]) * sizeof(float),
         /*.nb00         =*/ nb00,
         /*.nb01         =*/ nb01,
@@ -3197,9 +3413,6 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
     GGML_TENSOR_LOCALS( int32_t, ne,  op,         ne);
     GGML_TENSOR_LOCALS(uint64_t, nb,  op,         nb);
 
-    GGML_ASSERT(op->src[0]->type == GGML_TYPE_F32);
-    GGML_ASSERT(op->src[1]->type == GGML_TYPE_F32);
-
     GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
     GGML_ASSERT(ggml_is_contiguous_rows(op->src[1]));
 
@@ -3339,6 +3552,36 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
     return n_fuse;
 }
 
+int ggml_metal_op_silu_back(ggml_metal_op_t ctx, int idx) {
+    ggml_tensor * op = ctx->node(idx);
+
+    ggml_metal_library_t lib = ctx->lib;
+    ggml_metal_encoder_t enc = ctx->enc;
+
+    auto pipeline = ggml_metal_library_get_pipeline_silu_back(lib, op);
+
+    const int64_t ne = ggml_nelements(op);
+
+    ggml_metal_kargs_silu_back args = {
+        /*.ne =*/ ne,
+    };
+
+    int arg_idx{0};
+
+    ggml_metal_encoder_set_pipeline(enc, pipeline);
+    ggml_metal_encoder_set_bytes   (enc, &args, sizeof(args), arg_idx++);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[0]), arg_idx++);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op->src[1]), arg_idx++);
+    ggml_metal_encoder_set_buffer  (enc, ggml_metal_get_buffer_id(op), arg_idx++);
+
+    const int nth = std::min<int64_t>(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), ne);
+    const int64_t n = (ne + nth - 1) / nth;
+
+    ggml_metal_encoder_dispatch_threadgroups(enc, n, 1, 1, nth, 1, 1);
+
+    return 1;
+}
+
 int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) {
     ggml_tensor * op = ctx->node(idx);
 
@@ -3578,7 +3821,7 @@ int ggml_metal_op_norm(ggml_metal_op_t ctx, int idx) {
     }
 
     nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
-    nth = std::min(nth, args.ne00_t);
+    nth = std::min(nth, (args.ne00_t + 31)/32*32);
 
     const size_t smem = pipeline.smem;
 

ggml/src/ggml-metal/ggml-metal-ops.h

@@ -54,6 +54,8 @@ int ggml_metal_op_cumsum            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_get_rows          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_set_rows          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_diag              (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_lightning_indexer (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_dsv4_hc           (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_soft_max          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_ssm_conv          (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_ssm_scan          (ggml_metal_op_t ctx, int idx);
@@ -70,6 +72,7 @@ int ggml_metal_op_mul_mat_id        (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_add_id            (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_flash_attn_ext    (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_bin               (ggml_metal_op_t ctx, int idx);
+int ggml_metal_op_silu_back         (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_l2_norm           (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_group_norm        (ggml_metal_op_t ctx, int idx);
 int ggml_metal_op_norm              (ggml_metal_op_t ctx, int idx);

ggml/src/ggml-metal/ggml-metal.cpp

@@ -681,6 +681,7 @@ static void ggml_backend_metal_device_get_props(ggml_backend_dev_t dev, ggml_bac
         /* .host_buffer          = */ false,
         /* .buffer_from_host_ptr = */ true,
         /* .events               = */ true,
+        /* .mmap_support         = */ true,
     };
 }
 

ggml/src/ggml-metal/ggml-metal.metal

@@ -468,6 +468,34 @@ void quantize_iq4_nl(device const float * src, device block_iq4_nl & dst) {
     dst.d = sumq2 > 0 ? sumqx/sumq2 : d;
 }
 
+void quantize_tq2_0(device const float * src, device block_tq2_0 & dst) {
+#pragma METAL fp math_mode(safe)
+    float amax = 0.0f; // absolute max
+
+    for (int j = 0; j < QK_K; j++) {
+        const float v = src[j];
+        amax = MAX(amax, fabs(v));
+    }
+
+    const float d = amax;
+    const float id = d ? 1.0f/d : 0.0f;
+
+    dst.d = (half) d;
+
+    for (int j = 0; j < QK_K/4; j += 32) {
+        for (int m = 0; m < 32; ++m) {
+            uint8_t q = 0;
+            for (int n = 0; n < 4; ++n) {
+                // -1, 0, 1 -> 0, 1, 2
+                int xi = (int)round(src[m + n*32] * id) + 1;
+                q += (uint8_t)((xi & 3) << (2*n));
+            }
+            dst.qs[j + m] = q;
+        }
+        src += 4*32;
+    }
+}
+
 template <typename type4x4>
 void dequantize_q4_1(device const block_q4_1 * xb, short il, thread type4x4 & reg) {
     device const uint16_t * qs = ((device const uint16_t *)xb + 2);
@@ -1021,6 +1049,25 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4
     }
 }
 
+template <typename type4x4>
+void dequantize_tq2_0(device const block_tq2_0 * xb, short il, thread type4x4 & reg) {
+    device const uint8_t * qs = xb->qs;
+    const float d = xb->d;
+
+    float4x4 reg_f;
+
+    // 2 bits per element, 4 elements per byte, 128 elements per 32-byte group
+    const short base = il * 16;
+    for (int k = 0; k < 16; k++) {
+        const int i = base + k;
+        const int byte = ((i >> 7) & 1) * 32 + (i & 31);
+        const int l = (i >> 5) & 3;
+        reg_f[k/4][k%4] = d * (float)(((qs[byte] >> (2*l)) & 3) - 1);
+    }
+
+    reg = (type4x4) reg_f;
+}
+
 enum ggml_sort_order {
     GGML_SORT_ORDER_ASC,
     GGML_SORT_ORDER_DESC,
@@ -1255,6 +1302,20 @@ template [[host_name("kernel_unary_f32_f32_4")]] kernel kernel_unary_t kernel_un
 template [[host_name("kernel_unary_f16_f16")]]   kernel kernel_unary_t kernel_unary_impl<half,   half,   float>;
 template [[host_name("kernel_unary_f16_f16_4")]] kernel kernel_unary_t kernel_unary_impl<half4,  half4,  float4>;
 
+kernel void kernel_silu_back_f32(
+        constant ggml_metal_kargs_silu_back & args,
+        device const float * dy,
+        device const float * x,
+        device       float * dx,
+        uint gid [[thread_position_in_grid]]) {
+    if (gid >= args.ne) {
+        return;
+    }
+
+    const float s = 1.0f / (1.0f + exp(-x[gid]));
+    dx[gid] = dy[gid] * s * (1.0f + x[gid] * (1.0f - s));
+}
+
 // OP: 0 - add, 1 - sub, 2 - mul, 3 - div
 constant short FC_bin_op [[function_constant(FC_BIN + 0)]];
 constant short FC_bin_f  [[function_constant(FC_BIN + 1)]];
@@ -1418,6 +1479,8 @@ typedef decltype(kernel_bin_fuse_impl<float, float, float>) kernel_bin_fuse_t;
 
 template [[host_name("kernel_bin_fuse_f32_f32_f32")]]   kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float,  float,  float>;
 template [[host_name("kernel_bin_fuse_f32_f32_f32_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<float4, float4, float4>;
+template [[host_name("kernel_bin_fuse_f16_f16_f16")]]   kernel kernel_bin_fuse_t kernel_bin_fuse_impl<half,   half,   half>;
+template [[host_name("kernel_bin_fuse_f16_f16_f16_4")]] kernel kernel_bin_fuse_t kernel_bin_fuse_impl<half4,  half4,  half4>;
 
 kernel void kernel_add_id(
         constant ggml_metal_kargs_add_id & args,
@@ -2366,6 +2429,8 @@ kernel void kernel_ssm_scan_f32(
     const int32_t nh  = args.n_head;
     const int32_t ng  = args.n_group;
     const int32_t n_t = args.n_seq_tokens;
+    const int32_t n_s = args.n_seqs;
+    const int32_t K   = args.K;
 
     const int32_t s_off = args.s_off;
 
@@ -2424,6 +2489,12 @@ kernel void kernel_ssm_scan_f32(
             // recurse
             s0 = s;
 
+            const int32_t slot = n_t - 1 - (i2 + t);
+            if (slot > 0 && slot < K) {
+                device float * s_snapshot = (device float *) ((device char *) s_buff + (int64_t) slot*n_s*args.nb03);
+                s_snapshot[i] = s;
+            }
+
             B  += args.ns42;
             C  += args.ns52;
         }
@@ -7985,6 +8056,7 @@ template [[host_name("kernel_cpy_f32_q4_1")]]   kernel cpy_f_q_t kernel_cpy_f32_
 template [[host_name("kernel_cpy_f32_q5_0")]]   kernel cpy_f_q_t kernel_cpy_f32_q<QK5_0,  block_q5_0,   quantize_q5_0>;
 template [[host_name("kernel_cpy_f32_q5_1")]]   kernel cpy_f_q_t kernel_cpy_f32_q<QK5_1,  block_q5_1,   quantize_q5_1>;
 template [[host_name("kernel_cpy_f32_iq4_nl")]] kernel cpy_f_q_t kernel_cpy_f32_q<QK4_NL, block_iq4_nl, quantize_iq4_nl>;
+template [[host_name("kernel_cpy_f32_tq2_0")]]  kernel cpy_f_q_t kernel_cpy_f32_q<QK_K,   block_tq2_0,  quantize_tq2_0>;
 
 template<typename T4x4, typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread T4x4 &)>
 kernel void kernel_cpy_q_f32(
@@ -8032,6 +8104,8 @@ template [[host_name("kernel_cpy_q5_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<
 template [[host_name("kernel_cpy_q5_1_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q5_1, 2, dequantize_q5_1>;
 template [[host_name("kernel_cpy_q8_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_q8_0, 2, dequantize_q8_0>;
 
+template [[host_name("kernel_cpy_tq2_0_f32")]] kernel cpy_q_f_t kernel_cpy_q_f32<float4x4, block_tq2_0, QK_NL, dequantize_tq2_0>;
+
 template [[host_name("kernel_cpy_q1_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q1_0, 8, dequantize_q1_0>;
 template [[host_name("kernel_cpy_q2_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q2_0, 4, dequantize_q2_0>;
 template [[host_name("kernel_cpy_q4_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q4_0, 2, dequantize_q4_0>;
@@ -8040,6 +8114,8 @@ template [[host_name("kernel_cpy_q5_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<
 template [[host_name("kernel_cpy_q5_1_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q5_1, 2, dequantize_q5_1>;
 template [[host_name("kernel_cpy_q8_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_q8_0, 2, dequantize_q8_0>;
 
+template [[host_name("kernel_cpy_tq2_0_f16")]] kernel cpy_q_f_t kernel_cpy_q_f32<half4x4, block_tq2_0, QK_NL, dequantize_tq2_0>;
+
 template<typename T>
 kernel void kernel_concat(
         constant ggml_metal_kargs_concat & args,
@@ -9806,6 +9882,121 @@ kernel void kernel_mul_mv_mxfp4_f32(
     kernel_mul_mv_mxfp4_f32_impl<N_R0_MXFP4, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, shmem, tgpig, tiisg, sgitg);
 }
 
+template<int nr0, typename args_t>
+void kernel_mul_mv_tq2_0_f32_impl(
+        args_t args,
+        device const char * src0,
+        device const char * src1,
+        device       char * dst,
+        threadgroup  char * shmem,
+        uint3  tgpig,
+        ushort tiisg,
+        ushort sgitg) {
+    const short NSG = FC_mul_mv_nsg;
+
+    const int nb = args.ne00/QK_K;
+
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+
+    const int first_row = (r0 * NSG + sgitg) * nr0;
+
+    const uint i12 = im%FC_mul_mv_ne12;
+    const uint i13 = im/FC_mul_mv_ne12;
+
+    const uint64_t offset1 =        r1*args.nb11 + (i12        )*args.nb12 + (i13        )*args.nb13;
+
+    device const float * y = (device const float *) (src1 + offset1);
+
+    device const block_tq2_0 * ax[nr0];
+    for (int row = 0; row < nr0; ++row) {
+        const uint64_t offset0 = (first_row + row)*args.nb01 + (i12/FC_mul_mv_r2)*args.nb02 + (i13/FC_mul_mv_r3)*args.nb03;
+        ax[row] = (device const block_tq2_0 *) ((device char *) src0 + offset0);
+    }
+
+    float sumf[nr0] = {0.f};
+
+    // 8 threads per block, NBLOCK blocks per pass, 2 halves per block per pass
+    constexpr short NBLOCK = 4;
+
+    constexpr short NB = N_SIMDWIDTH/NBLOCK; // threads per block
+
+    const short blk = tiisg / NB;    // 0..NBLOCK-1, block handled by this thread
+    const short htg = tiisg % NB;    // 0..NB-1, thread within block (0..7)
+
+    // byte and y base offsets within the block (32 elements per thread, 4 per byte)
+    device const float4 * yb4 = (device const float4 *)(y + 4*htg + blk*QK_K);
+
+    // hoisted per-byte coefficients (from y) and total y-sum, shared across rows
+    // ref: https://github.com/ggml-org/llama.cpp/pull/26980
+    float4 coef[4];
+
+    for (int ib = blk; ib < nb; ib += NBLOCK) {
+        FOR_UNROLL (short h0 = 0; h0 < 2; ++h0) {
+            const float4 y0 = yb4[ 0 + 32*h0];
+            const float4 y1 = yb4[ 8 + 32*h0];
+            const float4 y2 = yb4[16 + 32*h0];
+            const float4 y3 = yb4[24 + 32*h0];
+
+            float sumy = 0.f;
+            FOR_UNROLL (short j = 0; j < 4; ++j) {
+                coef[j] = float4(
+                        y0[j],
+                        y1[j] - 4.0f*y0[j],
+                        y2[j] - 4.0f*y1[j],
+                        y3[j] - 4.0f*y2[j]);
+
+                sumy += (y0[j] + y1[j]) + (y2[j] + y3[j]);
+            }
+
+            FOR_UNROLL (short row = 0; row < nr0; ++row) {
+                device const block_tq2_0 & xb = ax[row][ib];
+                device const uchar * qs = xb.qs + 4*htg + 32*h0;
+
+                float sum = -sumy;
+                FOR_UNROLL (short j = 0; j < 4; ++j) {
+                    // express the 2-bit field shifts (v>>2, v>>4, v>>6) as float floor ops
+                    const float v = (float)qs[j];
+
+                    const float f0 = v;
+                    const float f1 = floor(v*0.25f);    // v>>2
+                    const float f2 = floor(v*0.0625);   // v>>4
+                    const float f3 = floor(v*0.015625); // v>>6
+
+                    sum += coef[j][0]*f0 + coef[j][1]*f1 + coef[j][2]*f2 + coef[j][3]*f3;
+                }
+
+                sumf[row] += xb.d * sum;
+            }
+        }
+
+        yb4 += QK_K * NBLOCK / 4;
+    }
+
+    device float * dst_f32 = (device float *) dst + (uint64_t)im*args.ne0*args.ne1 + (uint64_t)r1*args.ne0;
+
+    for (int row = 0; row < nr0; ++row) {
+        const float tot = simd_sum(sumf[row]);
+        if (tiisg == 0 && first_row + row < args.ne01) {
+            dst_f32[first_row + row] = tot;
+        }
+    }
+}
+
+[[host_name("kernel_mul_mv_tq2_0_f32")]]
+kernel void kernel_mul_mv_tq2_0_f32(
+        constant ggml_metal_kargs_mul_mv & args,
+        device const char * src0,
+        device const char * src1,
+        device       char * dst,
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
+
+    kernel_mul_mv_tq2_0_f32_impl<N_R0_TQ2_0, constant ggml_metal_kargs_mul_mv &>(args, src0, src1, dst, nullptr, tgpig, tiisg, sgitg);
+}
+
 template<typename block_q, short nl, void (*dequantize_func)(device const block_q *, short, thread float4x4 &)>
 kernel void kernel_get_rows_q(
         constant ggml_metal_kargs_get_rows & args,
@@ -9899,6 +10090,38 @@ template [[host_name("kernel_get_rows_iq1_s")]]   kernel get_rows_q_t kernel_get
 template [[host_name("kernel_get_rows_iq1_m")]]   kernel get_rows_q_t kernel_get_rows_q<block_iq1_m,   QK_NL, dequantize_iq1_m>;
 template [[host_name("kernel_get_rows_iq4_nl")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_nl,  2,     dequantize_iq4_nl>;
 template [[host_name("kernel_get_rows_iq4_xs")]]  kernel get_rows_q_t kernel_get_rows_q<block_iq4_xs,  QK_NL, dequantize_iq4_xs>;
+template [[host_name("kernel_get_rows_tq2_0")]]   kernel get_rows_q_t kernel_get_rows_q<block_tq2_0,   QK_NL, dequantize_tq2_0>;
+
+template<typename TS, typename TI, short QK, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
+kernel void kernel_set_rows_q(
+        constant ggml_metal_kargs_set_rows & args,
+        device const  void * src0,
+        device const  void * src1,
+        device       float * dst,
+        uint3                tgpig[[threadgroup_position_in_grid]],
+        uint                 tiitg[[thread_index_in_threadgroup]],
+        uint3                tptg [[threads_per_threadgroup]]) {
+    const int32_t i03 = tgpig.z;
+    const int32_t i02 = tgpig.y;
+
+    const int32_t i12 = i03%args.ne12;
+    const int32_t i11 = i02%args.ne11;
+
+    const int32_t i01 = tgpig.x*tptg.y + tiitg/tptg.x;
+    if (i01 >= args.ne01) {
+        return;
+    }
+
+    const int32_t i10 = i01;
+    const TI      i1  = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
+
+          device block_q * dst_row = (      device block_q *) ((      device char *) dst  +  i1*args.nb1  + i02*args.nb2  + i03*args.nb3);
+    const device TS      * src_row = (const device TS      *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
+
+    for (int ind = tiitg%tptg.x; ind < args.nk0; ind += tptg.x) {
+        quantize_func(src_row + QK*ind, dst_row[ind]);
+    }
+}
 
 template<typename TS, typename TI, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
 kernel void kernel_set_rows_q32(
@@ -9995,6 +10218,11 @@ template [[host_name("kernel_set_rows_f32_i32_q5_1")]]   kernel set_rows_q32_t k
 template [[host_name("kernel_set_rows_f32_i64_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int64_t, block_iq4_nl, quantize_iq4_nl>;
 template [[host_name("kernel_set_rows_f32_i32_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<float, int32_t, block_iq4_nl, quantize_iq4_nl>;
 
+typedef decltype(kernel_set_rows_q<float, int64_t, QK_K, block_tq2_0, quantize_tq2_0>) set_rows_qK_t;
+
+template [[host_name("kernel_set_rows_f32_i64_tq2_0")]]  kernel set_rows_qK_t kernel_set_rows_q<float, int64_t, QK_K, block_tq2_0, quantize_tq2_0>;
+template [[host_name("kernel_set_rows_f32_i32_tq2_0")]]  kernel set_rows_qK_t kernel_set_rows_q<float, int32_t, QK_K, block_tq2_0, quantize_tq2_0>;
+
 kernel void kernel_diag_f32(
         constant ggml_metal_kargs_diag & args,
         device   const char * src0,
@@ -10770,6 +10998,7 @@ template [[host_name("kernel_mul_mm_iq1_s_f32")]]   kernel mul_mm_t kernel_mul_m
 template [[host_name("kernel_mul_mm_iq1_m_f32")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_iq4_nl_f32")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_iq4_xs_f32")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  float, float2x4>;
+template [[host_name("kernel_mul_mm_tq2_0_f32")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  float, float2x4>;
 
 template [[host_name("kernel_mul_mm_f32_f16")]]     kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   float4x4,      1,     dequantize_f32,     float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_f16_f16")]]     kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   half4x4,       1,     dequantize_f16,     half,   half4x4,   half, half2x4>;
@@ -10795,6 +11024,7 @@ template [[host_name("kernel_mul_mm_iq1_s_f16")]]   kernel mul_mm_t kernel_mul_m
 template [[host_name("kernel_mul_mm_iq1_m_f16")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_iq4_nl_f16")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_iq4_xs_f16")]]  kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  half, half2x4>;
+template [[host_name("kernel_mul_mm_tq2_0_f16")]]   kernel mul_mm_t kernel_mul_mm<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  half, half2x4>;
 
 //
 // indirect matrix-matrix multiplication
@@ -10829,6 +11059,7 @@ template [[host_name("kernel_mul_mm_id_iq1_s_f32")]]   kernel mul_mm_id kernel_m
 template [[host_name("kernel_mul_mm_id_iq1_m_f32")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  float, float2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  float, float2x4>;
+template [[host_name("kernel_mul_mm_id_tq2_0_f32")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  float, float2x4>;
 
 template [[host_name("kernel_mul_mm_id_f32_f16")]]     kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   float4x4,      1,     dequantize_f32,     float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_id_f16_f16")]]     kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   half4x4,       1,     dequantize_f16,     half,   half4x4,   half, half2x4>;
@@ -10854,6 +11085,7 @@ template [[host_name("kernel_mul_mm_id_iq1_s_f16")]]   kernel mul_mm_id kernel_m
 template [[host_name("kernel_mul_mm_id_iq1_m_f16")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq1_m,   QK_NL, dequantize_iq1_m,   float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_nl_f16")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_nl,  2,     dequantize_iq4_nl,  float,  float4x4,  half, half2x4>;
 template [[host_name("kernel_mul_mm_id_iq4_xs_f16")]]  kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_iq4_xs,  QK_NL, dequantize_iq4_xs,  float,  float4x4,  half, half2x4>;
+template [[host_name("kernel_mul_mm_id_tq2_0_f16")]]   kernel mul_mm_id kernel_mul_mm_id<half,   half4x4,   simdgroup_half8x8,   half,   half2x4,   simdgroup_half8x8,   block_tq2_0,   QK_NL, dequantize_tq2_0,   float,  float4x4,  half, half2x4>;
 
 //
 // matrix-vector multiplication
@@ -11011,6 +11243,7 @@ template [[host_name("kernel_mul_mv_id_iq3_s_f32")]]   kernel kernel_mul_mv_id_t
 template [[host_name("kernel_mul_mv_id_iq2_s_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq2_s_f32_impl  <N_R0_IQ2_S>>>;
 template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_nl_f32_impl <N_R0_IQ4_NL>>>;
 template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]]  kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_iq4_xs_f32_impl <N_R0_IQ4_XS>>>;
+template [[host_name("kernel_mul_mv_id_tq2_0_f32")]]   kernel kernel_mul_mv_id_t kernel_mul_mv_id<mmv_fn<kernel_mul_mv_tq2_0_f32_impl  <N_R0_TQ2_0>>>;
 
 kernel void kernel_pool_2d_max_f32(
         constant    ggml_metal_kargs_pool_2d & args,
@@ -11278,3 +11511,310 @@ kernel void kernel_count_equal(
 typedef decltype(kernel_count_equal<int32_t>) kernel_count_equal_t;
 
 template [[host_name("kernel_count_equal_i32")]] kernel kernel_count_equal_t kernel_count_equal<int32_t>;
+
+template<
+    typename kd4x4_t,
+    short nl_k,
+    void (*deq_k)(device const kd4x4_t *, short, thread half4x4 &)>
+kernel void kernel_lightning_indexer(
+        constant ggml_metal_kargs_lightning_indexer & args,
+        device const char * q,
+        device const char * k,
+        device const char * w,
+        device const char * m,
+        device       char * dst,
+        uint3  tgpig[[threadgroup_position_in_grid]],
+        ushort tiitg[[thread_index_in_threadgroup]],
+        ushort tiisg[[thread_index_in_simdgroup]],
+        ushort sgitg[[simdgroup_index_in_threadgroup]]) {
+    constexpr short DK    = OP_LIGHTNING_INDEXER_DK;
+    constexpr short NH    = OP_LIGHTNING_INDEXER_NH;
+    constexpr short NHPTG = OP_LIGHTNING_INDEXER_NHPTG;
+    constexpr short NKPSG = OP_LIGHTNING_INDEXER_NKPSG;
+    constexpr short NSG   = OP_LIGHTNING_INDEXER_NSG;
+    constexpr short NBPTG = OP_LIGHTNING_INDEXER_NBPTG;
+
+    constexpr short DK4  = DK/4;
+    constexpr short DK8  = DK/8;
+    constexpr short DK16 = DK/16;
+
+    constexpr short NK  = NKPSG*NSG; // keys    per threadgroup
+    constexpr short NTG = 32*NSG;    // threads per threadgroup
+
+    const int i_stream = tgpig.z;
+    const int i_kv_0   = tgpig.x*NK;            // first key of this threadgroup
+    const int i_kv     = i_kv_0 + sgitg*NKPSG;  // first key of this simdgroup
+
+    threadgroup half sk[NK * DK16 * 16];
+    threadgroup half4x4 * sk4x4 = (threadgroup half4x4 *) sk;
+
+    for (short i = tiitg; i < NK*DK16; i += NTG) {
+        const short ik  = i/DK16;
+        const short i16 = i%DK16;
+
+        half4x4 tmp;
+
+        if (i_kv_0 + ik < args.n_kv) {
+            device const kd4x4_t * kr = (device const kd4x4_t *) (k + (i_kv_0 + ik)*args.nbk2 + i_stream*args.nbk3);
+
+            deq_k(kr + i16/nl_k, i16%nl_k, tmp);
+        } else {
+            FOR_UNROLL (short j = 0; j < 4; ++j) {
+                tmp[j] = half4(0.0h);
+            }
+        }
+
+        sk4x4[i] = tmp;
+    }
+
+    threadgroup_barrier(mem_flags::mem_threadgroup);
+
+    // K tile of this simdgroup, transposed to [DK, NKPSG]
+    simdgroup_half8x8 mk[DK8];
+
+    FOR_UNROLL (short i = 0; i < DK8; ++i) {
+        simdgroup_load(mk[i], sk + sgitg*NKPSG*DK + 8*i, DK, 0, true);
+    }
+
+    threadgroup half4   sq4[NHPTG*DK4];
+    threadgroup half  * sq = (threadgroup half *) sq4;
+
+    threadgroup float sw [NHPTG];
+    threadgroup float sqk[NSG*NHPTG*NKPSG];
+
+    const int i_batch_0 = tgpig.y*NBPTG;
+    const int n_batch   = min((int) NBPTG, args.n_batch - i_batch_0);
+
+    for (short ib = 0; ib < n_batch; ++ib) {
+        const int i_batch = i_batch_0 + ib;
+
+        device const char * pq = q + i_batch*args.nbq2 + i_stream*args.nbq3;
+        device const char * pw = w + i_batch*args.nbw1 + i_stream*args.nbw3;
+
+        float score = 0.0f;
+
+        FOR_UNROLL (short i_head = 0; i_head < NH; i_head += NHPTG) {
+            // stage the Q tile [DK, NHPTG] and the (prescaled) head weights
+            for (short i = tiitg; i < NHPTG*DK4; i += NTG) {
+                const short ih = i/DK4;
+                const short i4 = i%DK4;
+
+                device const float4 * q4 = (device const float4 *) (pq + (i_head + ih)*args.nbq1);
+
+                sq4[ih*DK4 + i4] = half4(q4[i4]);
+            }
+
+            if (tiitg < NHPTG) {
+                sw[tiitg] = ((device const float *) pw)[i_head + tiitg];
+            }
+
+            threadgroup_barrier(mem_flags::mem_threadgroup);
+
+            simdgroup_float8x8 mqk = make_filled_simdgroup_matrix<float, 8>(0.0f);
+
+            FOR_UNROLL (short i = 0; i < DK8; ++i) {
+                simdgroup_half8x8 mq;
+
+                simdgroup_load(mq, sq + 8*i, DK, 0, false);
+                simdgroup_multiply_accumulate(mqk, mq, mk[i], mqk);
+            }
+
+            threadgroup float * pqk = sqk + sgitg*NHPTG*NKPSG;
+
+            simdgroup_store(mqk, pqk, NKPSG, 0, false);
+            simdgroup_barrier(mem_flags::mem_threadgroup);
+
+            // one lane per key: ReLU, apply the head weight and accumulate over the head tile
+            if (tiisg < NKPSG) {
+                FOR_UNROLL (short ih = 0; ih < NHPTG; ++ih) {
+                    score += max(pqk[ih*NKPSG + tiisg], 0.0f)*sw[ih];
+                }
+            }
+
+            threadgroup_barrier(mem_flags::mem_threadgroup);
+        }
+
+        if (tiisg < NKPSG) {
+            const int ik = i_kv + tiisg;
+            if (ik < args.n_kv) {
+                device const half  * pm = (device const half  *) (m   + i_batch*args.nbm1 + (i_stream % args.mask_ne3)*args.nbm3);
+                device       float * pd = (device       float *) (dst + i_batch*args.nb1  + i_stream*args.nb3);
+
+                pd[ik] = score + (float) pm[ik];
+            }
+        }
+    }
+}
+
+typedef decltype(kernel_lightning_indexer<half4x4, 1, dequantize_f16>) kernel_lightning_indexer_t;
+
+template [[host_name("kernel_lightning_indexer_f32")]]  kernel kernel_lightning_indexer_t kernel_lightning_indexer<float4x4, 1, dequantize_f32>;
+template [[host_name("kernel_lightning_indexer_f16")]]  kernel kernel_lightning_indexer_t kernel_lightning_indexer<half4x4,  1, dequantize_f16>;
+
+#if defined(GGML_METAL_HAS_BF16)
+template [[host_name("kernel_lightning_indexer_bf16")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer<bfloat4x4, 1, dequantize_bf16>;
+#endif
+
+template [[host_name("kernel_lightning_indexer_q4_0")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer<block_q4_0, 2, dequantize_q4_0>;
+template [[host_name("kernel_lightning_indexer_q4_1")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer<block_q4_1, 2, dequantize_q4_1>;
+template [[host_name("kernel_lightning_indexer_q5_0")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer<block_q5_0, 2, dequantize_q5_0>;
+template [[host_name("kernel_lightning_indexer_q5_1")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer<block_q5_1, 2, dequantize_q5_1>;
+template [[host_name("kernel_lightning_indexer_q8_0")]] kernel kernel_lightning_indexer_t kernel_lightning_indexer<block_q8_0, 2, dequantize_q8_0>;
+
+kernel void kernel_dsv4_hc_comb_f32(
+        constant ggml_metal_kargs_dsv4_hc_comb & args,
+        device const char * mixes,
+        device const char * scale,
+        device const char * base,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort  tiisg[[thread_index_in_simdgroup]],
+        ushort  sgitg[[simdgroup_index_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    constexpr ushort hc = 4;
+    constexpr ushort comb_offset = 2*hc;
+
+    const int it = tgpig.x*ntg.y + sgitg;
+    if (it >= args.n_tokens) {
+        return;
+    }
+
+    float scale_lane = 0.0f;
+    if (tiisg == 0) {
+        scale_lane = *(device const float *) (scale + 2*args.nb_s0);
+    }
+    const float scale_comb = simd_shuffle(scale_lane, 0);
+
+    float v = 0.0f;
+    if (tiisg < hc*hc) {
+        v = *(device const float *) (mixes + (comb_offset + tiisg)*args.nb_m0 + it*args.nb_m1)*scale_comb
+          + *(device const float *) (base   + (comb_offset + tiisg)*args.nb_b0);
+    }
+
+    // Softmax across destinations (the four contiguous lanes for each source).
+    float vmax = max(v, simd_shuffle_xor(v, 1));
+    vmax = max(vmax, simd_shuffle_xor(vmax, 2));
+    v = exp(v - vmax);
+
+    float sum = v + simd_shuffle_xor(v, 1);
+    sum += simd_shuffle_xor(sum, 2);
+    v = v/sum + args.eps;
+
+    // Normalize columns: equal destination indices are four lanes apart.
+    sum = v + simd_shuffle_xor(v, 4);
+    sum += simd_shuffle_xor(sum, 8);
+    v /= sum + args.eps;
+
+    for (int i = 1; i < args.n_iter; ++i) {
+        sum = v + simd_shuffle_xor(v, 1);
+        sum += simd_shuffle_xor(sum, 2);
+        v /= sum + args.eps;
+
+        sum = v + simd_shuffle_xor(v, 4);
+        sum += simd_shuffle_xor(sum, 8);
+        v /= sum + args.eps;
+    }
+
+    if (tiisg < hc*hc) {
+        const ushort idst = tiisg & 3;
+        const ushort isrc = tiisg >> 2;
+        *(device float *) (dst + idst*args.nb_d0 + isrc*args.nb_d1 + it*args.nb_d2) = v;
+    }
+}
+
+kernel void kernel_dsv4_hc_pre_f32(
+        constant ggml_metal_kargs_dsv4_hc_pre & args,
+        device const char * x,
+        device const char * weights,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort  tiisg[[thread_index_in_simdgroup]],
+        ushort  sgitg[[simdgroup_index_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    constexpr ushort hc = 4;
+
+    const int it = tgpig.y;
+    const int i0 = ((int) tgpig.x*ntg.y + sgitg)*32 + tiisg;
+
+    float weight_lane = 0.0f;
+    if (tiisg < hc) {
+        weight_lane = *(device const float *) (weights + tiisg*args.nb_w0 + it*args.nb_w1);
+    }
+
+    float w[hc];
+    FOR_UNROLL (ushort ih = 0; ih < hc; ++ih) {
+        w[ih] = simd_shuffle(weight_lane, ih);
+    }
+
+    if (i0 >= args.n_embd) {
+        return;
+    }
+
+    device const char * xb = x + i0*args.nb_x0 + it*args.nb_x2;
+    float result = 0.0f;
+    FOR_UNROLL (ushort ih = 0; ih < hc; ++ih) {
+        result = fma(*(device const float *) (xb + ih*args.nb_x1), w[ih], result);
+    }
+
+    *(device float *) (dst + i0*args.nb_d0 + it*args.nb_d1) = result;
+}
+
+kernel void kernel_dsv4_hc_post_f32(
+        constant ggml_metal_kargs_dsv4_hc_post & args,
+        device const char * x,
+        device const char * residual,
+        device const char * post,
+        device const char * comb,
+        device       char * dst,
+        uint3   tgpig[[threadgroup_position_in_grid]],
+        ushort  tiisg[[thread_index_in_simdgroup]],
+        ushort  sgitg[[simdgroup_index_in_threadgroup]],
+        ushort3   ntg[[threads_per_threadgroup]]) {
+    constexpr ushort hc = 4;
+
+    const int it = tgpig.y;
+    const int i0 = ((int) tgpig.x*ntg.y + sgitg)*32 + tiisg;
+
+    float coeff_lane = 0.0f;
+    if (tiisg < hc) {
+        coeff_lane = *(device const float *) (post + tiisg*args.nb_p0 + it*args.nb_p1);
+    } else if (tiisg < hc + hc*hc) {
+        const ushort idx  = tiisg - hc;
+        const ushort idst = idx & 3;
+        const ushort isrc = idx >> 2;
+        coeff_lane = *(device const float *) (comb + idst*args.nb_c0 + isrc*args.nb_c1 + it*args.nb_c2);
+    }
+
+    float post_reg[hc];
+    float comb_reg[hc][hc];
+    FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) {
+        post_reg[idst] = simd_shuffle(coeff_lane, idst);
+    }
+    FOR_UNROLL (ushort isrc = 0; isrc < hc; ++isrc) {
+        FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) {
+            comb_reg[isrc][idst] = simd_shuffle(coeff_lane, hc + idst + hc*isrc);
+        }
+    }
+
+    if (i0 >= args.n_embd) {
+        return;
+    }
+
+    const float xv = *(device const float *) (x + i0*args.nb_x0 + it*args.nb_x1);
+    float result[hc];
+    FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) {
+        result[idst] = xv*post_reg[idst];
+    }
+
+    device const char * rb = residual + i0*args.nb_r0 + it*args.nb_r2;
+    FOR_UNROLL (ushort isrc = 0; isrc < hc; ++isrc) {
+        const float rv = *(device const float *) (rb + isrc*args.nb_r1);
+        FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) {
+            result[idst] = fma(rv, comb_reg[isrc][idst], result[idst]);
+        }
+    }
+
+    FOR_UNROLL (ushort idst = 0; idst < hc; ++idst) {
+        *(device float *) (dst + i0*args.nb_d0 + idst*args.nb_d1 + it*args.nb_d2) = result[idst];
+    }
+}

ggml/src/ggml-rpc/ggml-rpc.cpp

@@ -1884,6 +1884,7 @@ static void ggml_backend_rpc_device_get_props(ggml_backend_dev_t dev, struct ggm
         /* .host_buffer           = */ false,
         /* .buffer_from_host_ptr  = */ false,
         /* .events                = */ false,
+        /* .mmap_support          = */ true,
     };
 }
 

ggml/src/ggml-sycl/dsv4-hc.cpp

@@ -0,0 +1,280 @@
+#include "ggml-impl.h"
+#include "dsv4-hc.hpp"
+
+#include <cmath>
+
+static constexpr int DSV4_HC = 4;
+
+static void dsv4_hc_pre_f32_sycl(
+        const float * x, const float * weights, float * dst,
+        int64_t n_embd, int64_t hc, int64_t n_tokens,
+        int64_t sx0, int64_t sx1, int64_t sx2,
+        int64_t sw0, int64_t sw1,
+        int64_t sd0, int64_t sd1,
+        queue_ptr stream) {
+    const int64_t nr = n_embd * n_tokens;
+    const int64_t block_size = 256;
+    const int64_t num_blocks = (nr + block_size - 1) / block_size;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(sycl::range<1>(num_blocks * block_size), sycl::range<1>(block_size)),
+        [=](sycl::nd_item<1> item) {
+            const int64_t ir = item.get_global_id(0);
+            if (ir >= nr) {
+                return;
+            }
+
+            const int64_t i0 = ir % n_embd;
+            const int64_t it = ir / n_embd;
+
+            float sum = x[i0*sx0 + it*sx2] * weights[it*sw1];
+            for (int64_t ih = 1; ih < hc; ++ih) {
+                const float xv = x[i0*sx0 + ih*sx1 + it*sx2];
+                const float wv = weights[ih*sw0 + it*sw1];
+                sum += xv * wv;
+            }
+
+            dst[i0*sd0 + it*sd1] = sum;
+        });
+}
+
+static void dsv4_hc_comb_norm_cols(float * comb, float eps) {
+    for (int idst = 0; idst < DSV4_HC; ++idst) {
+        float sum = eps;
+        for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+            sum += comb[idst + DSV4_HC*isrc];
+        }
+
+        const float inv_sum = 1.0f / sum;
+        for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+            comb[idst + DSV4_HC*isrc] *= inv_sum;
+        }
+    }
+}
+
+static void dsv4_hc_comb_norm_rows(float * comb, float eps) {
+    for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+        float sum = eps;
+        for (int idst = 0; idst < DSV4_HC; ++idst) {
+            sum += comb[idst + DSV4_HC*isrc];
+        }
+
+        const float inv_sum = 1.0f / sum;
+        for (int idst = 0; idst < DSV4_HC; ++idst) {
+            comb[idst + DSV4_HC*isrc] *= inv_sum;
+        }
+    }
+}
+
+static void dsv4_hc_comb_f32_sycl(
+        const float * mixes,
+        const float * scale,
+        const float * base,
+        float * dst,
+        int64_t n_tokens,
+        int64_t sm0,
+        int64_t sm1,
+        int64_t ss0,
+        int64_t sb0,
+        int64_t sd0,
+        int64_t sd1,
+        int64_t sd2,
+        float eps,
+        int32_t n_iter,
+        queue_ptr stream) {
+    constexpr int comb_offset = 2*DSV4_HC;
+
+    const int64_t block_size = 256;
+    const int64_t num_blocks = (n_tokens + block_size - 1) / block_size;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(sycl::range<1>(num_blocks * block_size), sycl::range<1>(block_size)),
+        [=](sycl::nd_item<1> item_ct1) {
+            const int64_t it = item_ct1.get_global_id(0);
+
+            if (it >= n_tokens) {
+                return;
+            }
+
+            const float scale_comb = scale[2*ss0];
+            float comb[DSV4_HC*DSV4_HC];
+
+            for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+                float max = -INFINITY;
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    const float v = mixes[(comb_offset + idx)*sm0 + it*sm1] * scale_comb + base[(comb_offset + idx)*sb0];
+                    comb[idx] = v;
+                    max = fmaxf(max, v);
+                }
+
+                float sum = 0.0f;
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    const float v = expf(comb[idx] - max);
+                    comb[idx] = v;
+                    sum += v;
+                }
+
+                const float inv_sum = 1.0f / sum;
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    comb[idx] = comb[idx] * inv_sum + eps;
+                }
+            }
+
+            dsv4_hc_comb_norm_cols(comb, eps);
+            for (int32_t i = 1; i < n_iter; ++i) {
+                dsv4_hc_comb_norm_rows(comb, eps);
+                dsv4_hc_comb_norm_cols(comb, eps);
+            }
+
+            for (int isrc = 0; isrc < DSV4_HC; ++isrc) {
+                for (int idst = 0; idst < DSV4_HC; ++idst) {
+                    const int idx = idst + DSV4_HC*isrc;
+                    dst[idst*sd0 + isrc*sd1 + it*sd2] = comb[idx];
+                }
+            }
+        });
+}
+
+static void dsv4_hc_post_f32_sycl(
+        const float * x, const float * residual, const float * post, const float * comb, float * dst,
+        int64_t n_embd, int64_t hc, int64_t n_tokens,
+        int64_t sx0, int64_t sx1,
+        int64_t sr0, int64_t sr1, int64_t sr2,
+        int64_t sp0, int64_t sp1,
+        int64_t sc0, int64_t sc1, int64_t sc2,
+        int64_t sd0, int64_t sd1, int64_t sd2,
+        queue_ptr stream) {
+    const int64_t nr = n_embd * hc * n_tokens;
+    const int64_t block_size = 256;
+    const int64_t num_blocks = (nr + block_size - 1) / block_size;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(sycl::range<1>(num_blocks * block_size), sycl::range<1>(block_size)),
+        [=](sycl::nd_item<1> item) {
+            const int64_t ir = item.get_global_id(0);
+            if (ir >= nr) {
+                return;
+            }
+
+            const int64_t i0   = ir % n_embd;
+            const int64_t idst = (ir / n_embd) % hc;
+            const int64_t it   = ir / (n_embd * hc);
+
+            float sum = x[i0*sx0 + it*sx1] * post[idst*sp0 + it*sp1];
+            for (int64_t isrc = 0; isrc < hc; ++isrc) {
+                sum += residual[i0*sr0 + isrc*sr1 + it*sr2] * comb[idst*sc0 + isrc*sc1 + it*sc2];
+            }
+
+            dst[i0*sd0 + idst*sd1 + it*sd2] = sum;
+        });
+}
+
+void ggml_sycl_op_dsv4_hc_pre(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
+    const ggml_tensor * x       = dst->src[0];
+    const ggml_tensor * weights = dst->src[1];
+
+    GGML_ASSERT(x->type == GGML_TYPE_F32);
+    GGML_ASSERT(weights->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_LOCALS(size_t, nbx, x,       nb);
+    GGML_TENSOR_LOCALS(size_t, nbw, weights, nb);
+    GGML_TENSOR_LOCALS(size_t, nbd, dst,     nb);
+
+    const int64_t n_embd   = x->ne[0];
+    const int64_t hc       = x->ne[1];
+    const int64_t n_tokens = x->ne[2];
+
+    queue_ptr stream = ctx.stream();
+
+    dsv4_hc_pre_f32_sycl(
+            (const float *) x->data, (const float *) weights->data, (float *) dst->data,
+            n_embd, hc, n_tokens,
+            nbx0 / sizeof(float), nbx1 / sizeof(float), nbx2 / sizeof(float),
+            nbw0 / sizeof(float), nbw1 / sizeof(float),
+            nbd0 / sizeof(float), nbd1 / sizeof(float),
+            stream);
+}
+
+void ggml_sycl_op_dsv4_hc_comb(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/3);
+
+    const ggml_tensor * mixes = dst->src[0];
+    const ggml_tensor * scale = dst->src[1];
+    const ggml_tensor * base  = dst->src[2];
+
+    GGML_ASSERT(mixes->type == GGML_TYPE_F32);
+    GGML_ASSERT(scale->type == GGML_TYPE_F32);
+    GGML_ASSERT(base->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    constexpr int64_t hc_mix_dim = (2 + DSV4_HC)*DSV4_HC;
+
+    GGML_ASSERT(mixes->ne[0] == hc_mix_dim);
+    GGML_ASSERT(dst->ne[0] == DSV4_HC);
+    GGML_ASSERT(dst->ne[1] == DSV4_HC);
+    GGML_ASSERT(dst->ne[2] == mixes->ne[1]);
+    GGML_ASSERT(scale->ne[0] >= 3);
+    GGML_ASSERT(base->ne[0] == hc_mix_dim);
+
+    GGML_TENSOR_LOCALS(size_t, nbm, mixes, nb);
+    GGML_TENSOR_LOCALS(size_t, nbs, scale, nb);
+    GGML_TENSOR_LOCALS(size_t, nbb, base,  nb);
+    GGML_TENSOR_LOCALS(size_t, nbd, dst,   nb);
+
+    const int64_t n_tokens = mixes->ne[1];
+    const float eps = ggml_get_op_params_f32(dst, 0);
+    const int32_t n_iter = ggml_get_op_params_i32(dst, 1);
+
+    queue_ptr stream = ctx.stream();
+
+    dsv4_hc_comb_f32_sycl(
+            (const float *) mixes->data, (const float *) scale->data, (const float *) base->data, (float *) dst->data,
+            n_tokens,
+            nbm0 / sizeof(float), nbm1 / sizeof(float),
+            nbs0 / sizeof(float),
+            nbb0 / sizeof(float),
+            nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float),
+            eps, n_iter, stream);
+}
+
+void ggml_sycl_op_dsv4_hc_post(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/4);
+    const ggml_tensor * x        = dst->src[0];
+    const ggml_tensor * residual = dst->src[1];
+    const ggml_tensor * post     = dst->src[2];
+    const ggml_tensor * comb     = dst->src[3];
+
+    GGML_ASSERT(x->type == GGML_TYPE_F32);
+    GGML_ASSERT(residual->type == GGML_TYPE_F32);
+    GGML_ASSERT(post->type == GGML_TYPE_F32);
+    GGML_ASSERT(comb->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+
+    GGML_TENSOR_LOCALS(size_t, nbx, x,        nb);
+    GGML_TENSOR_LOCALS(size_t, nbr, residual, nb);
+    GGML_TENSOR_LOCALS(size_t, nbp, post,     nb);
+    GGML_TENSOR_LOCALS(size_t, nbc, comb,     nb);
+    GGML_TENSOR_LOCALS(size_t, nbd, dst,      nb);
+
+    const int64_t n_embd   = x->ne[0];
+    const int64_t n_tokens = x->ne[1];
+    const int64_t hc       = residual->ne[1];
+
+    queue_ptr stream = ctx.stream();
+
+    dsv4_hc_post_f32_sycl(
+            (const float *) x->data, (const float *) residual->data,
+            (const float *) post->data, (const float *) comb->data, (float *) dst->data,
+            n_embd, hc, n_tokens,
+            nbx0 / sizeof(float), nbx1 / sizeof(float),
+            nbr0 / sizeof(float), nbr1 / sizeof(float), nbr2 / sizeof(float),
+            nbp0 / sizeof(float), nbp1 / sizeof(float),
+            nbc0 / sizeof(float), nbc1 / sizeof(float), nbc2 / sizeof(float),
+            nbd0 / sizeof(float), nbd1 / sizeof(float), nbd2 / sizeof(float),
+            stream);
+}

ggml/src/ggml-sycl/dsv4-hc.hpp

@@ -0,0 +1,10 @@
+#ifndef GGML_SYCL_DSV4_HC_HPP
+#define GGML_SYCL_DSV4_HC_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_dsv4_hc_pre(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+void ggml_sycl_op_dsv4_hc_comb(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+void ggml_sycl_op_dsv4_hc_post(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_DSV4_HC_HPP

ggml/src/ggml-sycl/lightning-indexer.cpp

@@ -0,0 +1,197 @@
+#include "lightning-indexer.hpp"
+#include "dequantize.hpp"
+
+static void lightning_indexer_f32_sycl(
+        const char * q, const char * k, const char * w, const char * m, float * dst,
+        int64_t n_embd, int64_t n_head, int64_t n_batch, int64_t n_stream, int64_t n_kv,
+        int64_t nem3,
+        int64_t nbq1, int64_t nbq2, int64_t nbq3,
+        int64_t nbk2, int64_t nbk3,
+        int64_t nbw1, int64_t nbw3,
+        int64_t nbm1, int64_t nbm3,
+        int64_t nb1, int64_t nb3,
+        ggml_type k_type,
+        queue_ptr stream) {
+
+    constexpr int64_t LANES = WARP_SIZE;
+    constexpr int64_t ELEMS_PER_LANE = 8;
+    constexpr int64_t ROWS_PER_BLOCK = 4;
+    constexpr int64_t BLOCK_SIZE = ROWS_PER_BLOCK * LANES;
+
+    const int64_t n_rows = n_batch * n_stream * n_kv;
+    const int64_t n_blocks = (n_rows + ROWS_PER_BLOCK - 1) / ROWS_PER_BLOCK;
+
+    stream->parallel_for(
+        sycl::nd_range<1>(
+            sycl::range<1>(n_blocks * BLOCK_SIZE),
+            sycl::range<1>(BLOCK_SIZE)),
+        [=](sycl::nd_item<1> item) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+            const int64_t ir   = item.get_global_id(0);
+            const int64_t lane = ir % LANES;
+            const int64_t row  = ir / LANES;
+            if (row >= n_rows) {
+                return;
+            }
+
+            const int64_t i_bs     = row / n_kv;
+            const int64_t i_kv     = row % n_kv;
+            const int64_t i_batch  = i_bs / n_stream;
+            const int64_t i_stream = i_bs % n_stream;
+
+            // load K row slice into registers (row is contiguous, nbk0 == type size)
+            const char * k_base = k + i_kv*nbk2 + i_stream*nbk3;
+            float k_local[ELEMS_PER_LANE];
+            if (k_type == GGML_TYPE_F16) {
+                const sycl::half * k_row = (const sycl::half *) k_base;
+#pragma unroll
+                for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                    k_local[j] = static_cast<float>(k_row[lane*ELEMS_PER_LANE + j]);
+                }
+            } else if (k_type == GGML_TYPE_F32) {
+                const float * k_row = (const float *) k_base;
+#pragma unroll
+                for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                    k_local[j] = k_row[lane*ELEMS_PER_LANE + j];
+                }
+            } else {
+                const int64_t lane_base = lane * ELEMS_PER_LANE;
+                switch (k_type) {
+                    case GGML_TYPE_BF16: {
+                        const sycl::ext::oneapi::bfloat16 * k_row = (const sycl::ext::oneapi::bfloat16 *) k_base;
+#pragma unroll
+                        for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                            k_local[j] = static_cast<float>(k_row[lane_base + j]);
+                        }
+                    } break;
+                    case GGML_TYPE_Q4_0:
+                    case GGML_TYPE_Q4_1:
+                    case GGML_TYPE_Q5_0:
+                    case GGML_TYPE_Q5_1: {
+#pragma unroll
+                        for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                            const int64_t idx = lane_base + j;
+                            const int64_t ib  = idx / QK4_0;
+                            const int iqs     = idx % (QK4_0/2);
+                            dfloat2 kv;
+                            if (k_type == GGML_TYPE_Q4_0) {
+                                dequantize_q4_0(k_base, ib, iqs, kv);
+                            } else if (k_type == GGML_TYPE_Q4_1) {
+                                dequantize_q4_1(k_base, ib, iqs, kv);
+                            } else if (k_type == GGML_TYPE_Q5_0) {
+                                dequantize_q5_0(k_base, ib, iqs, kv);
+                            } else {
+                                dequantize_q5_1(k_base, ib, iqs, kv);
+                            }
+                            k_local[j] = (idx % QK4_0) < (QK4_0/2) ? static_cast<float>(kv.x()) : static_cast<float>(kv.y());
+                        }
+                    } break;
+                    case GGML_TYPE_Q8_0: {
+#pragma unroll
+                        for (int64_t pair = 0; pair < ELEMS_PER_LANE / 2; ++pair) {
+                            const int64_t elem0 = lane_base + 2 * pair;
+                            dfloat2 kv;
+                            dequantize_q8_0(k_base, elem0 / QK8_0, elem0 % QK8_0, kv);
+                            k_local[2 * pair + 0] = static_cast<float>(kv.x());
+                            k_local[2 * pair + 1] = static_cast<float>(kv.y());
+                        }
+                    } break;
+                    case GGML_TYPE_IQ4_NL: {
+#pragma unroll
+                        for (int64_t pair = 0; pair < ELEMS_PER_LANE / 2; ++pair) {
+                            const int64_t elem0 = lane_base + 2 * pair;
+                            dfloat2 kv;
+                            dequantize_iq4_nl(k_base, elem0 / QK4_NL, elem0 % QK4_NL, kv);
+                            k_local[2 * pair + 0] = static_cast<float>(kv.x());
+                            k_local[2 * pair + 1] = static_cast<float>(kv.y());
+                        }
+                    } break;
+                    default:
+#pragma unroll
+                        for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                            k_local[j] = 0.0f;
+                        }
+                        break;
+                }
+            }
+
+            const char  * q_base = q + i_batch*nbq2 + i_stream*nbq3;
+            const float * w_base = (const float *) (w + i_batch*nbw1 + i_stream*nbw3);
+
+            float score = 0.0f;
+            for (int64_t h = 0; h < n_head; ++h) {
+                const float * q_row = (const float *) (q_base + h*nbq1);
+                float dot = 0.0f;
+#pragma unroll
+                for (int64_t j = 0; j < ELEMS_PER_LANE; ++j) {
+                    const int64_t i = lane*ELEMS_PER_LANE + j;
+                    if (i < n_embd) {
+                        dot += q_row[i] * k_local[j];
+                    }
+                }
+                dot = sycl::reduce_over_group(item.get_sub_group(), dot, sycl::plus<float>());
+                if (lane == 0) {
+                    score += sycl::max(dot, 0.0f) * w_base[h];
+                }
+            }
+
+            if (lane == 0) {
+                const sycl::half * m_base = (const sycl::half *) (m + i_batch*nbm1 + (i_stream % nem3)*nbm3);
+                // flat-index store: storing through a strided base pointer
+                // hangs/misroutes writes on this stack when n_batch*n_stream > 1
+                const int64_t dst_idx = i_kv + i_batch*(nb1/sizeof(float)) + i_stream*(nb3/sizeof(float));
+                dst[dst_idx] = score + static_cast<float>(m_base[i_kv]);
+            }
+        });
+}
+
+void ggml_sycl_op_lightning_indexer(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
+    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/4);
+    const ggml_tensor * q = dst->src[0];
+    const ggml_tensor * k = dst->src[1];
+    const ggml_tensor * w = dst->src[2]; // weights
+    const ggml_tensor * m = dst->src[3]; // mask
+
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(  q->type == GGML_TYPE_F32);
+    GGML_ASSERT(  w->type == GGML_TYPE_F32);
+    GGML_ASSERT(  m->type == GGML_TYPE_F16);
+    GGML_ASSERT(k->type == GGML_TYPE_F16 || k->type == GGML_TYPE_F32 || k->type == GGML_TYPE_BF16 ||
+                k->type == GGML_TYPE_Q8_0 || k->type == GGML_TYPE_Q5_1 || k->type == GGML_TYPE_Q5_0 ||
+                k->type == GGML_TYPE_Q4_1 || k->type == GGML_TYPE_Q4_0 || k->type == GGML_TYPE_IQ4_NL);
+
+    GGML_TENSOR_LOCALS(int64_t, neq, q, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbq, q, nb);
+    GGML_TENSOR_LOCALS(int64_t, nek, k, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbk, k, nb);
+    GGML_TENSOR_LOCALS(size_t,  nbw, w, nb);
+    GGML_TENSOR_LOCALS(int64_t, nem, m, ne);
+    GGML_TENSOR_LOCALS(size_t,  nbm, m, nb);
+    GGML_TENSOR_LOCALS(int64_t, ne, dst, ne);
+    GGML_TENSOR_LOCALS(size_t,  nb, dst, nb);
+
+    // input rows must be contiguous
+    GGML_ASSERT(nbq0 == ggml_type_size(q->type));
+    GGML_ASSERT(nbk0 == ggml_type_size(k->type));
+    GGML_ASSERT(nbm0 == ggml_type_size(m->type));
+    GGML_ASSERT(nb0  == ggml_type_size(dst->type));
+
+    const int64_t n_embd   = neq0;
+    const int64_t n_head   = neq1;
+    const int64_t n_batch  = neq2;
+    const int64_t n_stream = neq3;
+    const int64_t n_kv     = nek2;
+
+    GGML_ASSERT(n_embd == WARP_SIZE * 8);
+
+    lightning_indexer_f32_sycl(
+            (const char *) q->data, (const char *) k->data,
+            (const char *) w->data, (const char *) m->data, (float *) dst->data,
+            n_embd, n_head, n_batch, n_stream, n_kv, nem3,
+            nbq1, nbq2, nbq3,
+            nbk2, nbk3,
+            nbw1, nbw3,
+            nbm1, nbm3,
+            nb1, nb3,
+            k->type,
+            ctx.stream());
+}

ggml/src/ggml-sycl/lightning-indexer.hpp

@@ -0,0 +1,8 @@
+#ifndef GGML_SYCL_LIGHTNING_INDEXER_HPP
+#define GGML_SYCL_LIGHTNING_INDEXER_HPP
+
+#include "common.hpp"
+
+void ggml_sycl_op_lightning_indexer(ggml_backend_sycl_context & ctx, ggml_tensor * dst);
+
+#endif // GGML_SYCL_LIGHTNING_INDEXER_HPP

ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs.glsl

@@ -608,6 +608,20 @@ vec2 get_dm(uint ib, uint a_offset) {
 }
 #endif
 
+#if defined(DATA_A_TQ2_0)
+vec2 dequantize(uint ib, uint iqs, uint a_offset) {
+    // elem e -> byte qs[(e/128)*32 + e%32], bits 2*((e%128)/32); w = q - 1 (d applied via get_dm)
+    const uint qsi   = (iqs / 128) * 32 + (iqs % 32);  // iqs even -> qsi, qsi+1 in same group/level
+    const uint shift = 2 * ((iqs % 128) / 32);
+
+    const uvec2 qs = uvec2(data_a[a_offset + ib].qs[qsi], data_a[a_offset + ib].qs[qsi + 1]);
+    return vec2((qs >> shift) & 3) - 1.0;
+}
+vec2 get_dm(uint ib, uint a_offset) {
+    return vec2(float(data_a[a_offset + ib].d), 0);
+}
+#endif
+
 #if defined(DATA_A_Q3_K)
 vec2 dequantize(uint ib, uint iqs, uint a_offset) {
     iqs /= 2;

ggml/src/ggml-vulkan/vulkan-shaders/dequant_funcs_cm2.glsl

@@ -247,6 +247,44 @@ f16vec4 dequantFuncQ8_0_v(const in decodeBufQ8_0 bl, const in uint blockCoords[2
     return f16vec4(vec4(qi) * vec4(float(d)));
 }
 
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufTQ2_0 {
+   block_tq2_0 block;
+};
+
+layout(buffer_reference, std430, buffer_reference_align = 2) buffer decodeBufTQ2_0_packed16 {
+   block_tq2_0_packed16 block;
+};
+
+float16_t dequantFuncTQ2_0(const in decodeBufTQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    decodeBufTQ2_0_packed16 bl16 = decodeBufTQ2_0_packed16(bl);
+    const uint idx = coordInBlock[1];
+
+    const uint qsshift = (idx & 0x60) >> 4;             // 0,2,4,6
+
+    uint qs = uint32_t(bl16.block.qs[((idx & 0x80) >> 3) + ((idx & 0x1E) >> 1)]);
+    qs = (qs >> qsshift) & 0x0303;
+    qs = unpack8(qs)[idx & 1];
+
+    return bl.block.d * (float16_t(int(qs)) - float16_t(1.0));
+}
+
+f16vec4 dequantFuncTQ2_0_v(const in decodeBufTQ2_0 bl, const in uint blockCoords[2], const in uint coordInBlock[2])
+{
+    const uint idx = coordInBlock[1];
+
+    const uint qsshift = (idx & 0x60) >> 4;             // 0,2,4,6
+    const uint qsi = ((idx & 0x80) >> 2) + (idx & 0x1C); // byte index of 4-aligned group
+
+    const uint qsw = (uint(bl.block.qs[qsi]))
+                   | (uint(bl.block.qs[qsi + 1]) << 8)
+                   | (uint(bl.block.qs[qsi + 2]) << 16)
+                   | (uint(bl.block.qs[qsi + 3]) << 24);
+    const u8vec4 q = unpack8((qsw >> qsshift) & 0x03030303);
+
+    return bl.block.d * (f16vec4(q) - f16vec4(1.0));
+}
+
 layout(buffer_reference, std430, buffer_reference_align = 4) buffer decodeBufQ2_K {
    block_q2_K block;
 };
@@ -1368,6 +1406,9 @@ f16vec4 dequantFuncNVFP4_v(const in decodeBufNVFP4 bl, const in uint blockCoords
 #elif defined(DATA_A_Q8_0)
 #define dequantFuncA dequantFuncQ8_0
 #define dequantFuncA_v dequantFuncQ8_0_v
+#elif defined(DATA_A_TQ2_0)
+#define dequantFuncA dequantFuncTQ2_0
+#define dequantFuncA_v dequantFuncTQ2_0_v
 #elif defined(DATA_A_Q2_K)
 #define dequantFuncA dequantFuncQ2_K
 #define dequantFuncA_v dequantFuncQ2_K_v

ggml/src/ggml-vulkan/vulkan-shaders/dequant_tq2_0.comp

@@ -0,0 +1,31 @@
+#version 450
+
+#include "dequant_head.glsl"
+
+layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in;
+
+layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
+layout (binding = 1) writeonly buffer D {D_TYPE data_b[];};
+
+void main() {
+    [[unroll]] for (uint wgy = 0; wgy < 256; wgy++) {
+        const uint i = gl_WorkGroupID.x * 256 + wgy;
+        if (i >= p.nel / QUANT_K) {
+            return;
+        }
+
+        const uint tid = gl_LocalInvocationID.x;
+        const uint ip = tid / 32;         // group 0,1 (128 elems each)
+        const uint il = tid - 32 * ip;    // byte in group 0..31
+
+        const uint y_idx = i * QUANT_K + 128 * ip + il;
+
+        const uint8_t qs = data_a[i].qs[32 * ip + il];
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d);
+        data_b[y_idx +  0] = D_TYPE(d * FLOAT_TYPE(int((qs >> 0) & 3) - 1));
+        data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(int((qs >> 2) & 3) - 1));
+        data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(int((qs >> 4) & 3) - 1));
+        data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(int((qs >> 6) & 3) - 1));
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/gla.comp

@@ -0,0 +1,82 @@
+#version 450
+
+#extension GL_EXT_control_flow_attributes : require
+
+#define BLOCK_SIZE 64
+layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in;
+
+layout(push_constant) uniform Parameters {
+    uint B;
+    uint T;
+    uint C;
+    uint H;
+    float scale;
+};
+
+layout(binding = 0) readonly buffer KBuf { A_TYPE k[]; };
+layout(binding = 1) readonly buffer VBuf { A_TYPE v[]; };
+layout(binding = 2) readonly buffer QBuf { A_TYPE q[]; };
+layout(binding = 3) readonly buffer GBuf { A_TYPE g[]; };
+layout(binding = 4) readonly buffer StateBuf { A_TYPE state_in[]; };
+layout(binding = 5) buffer DstBuf { A_TYPE dst[]; };
+
+shared A_TYPE _k[BLOCK_SIZE], _q[BLOCK_SIZE], _g[BLOCK_SIZE];
+
+void main() {
+    const uint head_size = BLOCK_SIZE;
+    const uint batch_id = gl_WorkGroupID.x / H;
+    const uint head_id = gl_WorkGroupID.x % H;
+    const uint tid = gl_LocalInvocationID.x;
+
+    const uint state_size = C * head_size;
+    const uint n_seq_tokens = T / B;
+
+    if (batch_id >= B || head_id >= H) {
+        return;
+    }
+
+    // state[i] holds column tid of this head's state matrix: S[i][tid]
+    A_TYPE state[BLOCK_SIZE];
+    [[unroll]] for (uint i = 0; i < head_size; i++) {
+        state[i] = state_in[batch_id * state_size + head_id * head_size * head_size
+                          + i * head_size + tid];
+    }
+
+    const uint start_t = batch_id * n_seq_tokens * C + head_id * head_size + tid;
+    const uint end_t = (batch_id + 1) * n_seq_tokens * C + head_id * head_size + tid;
+
+    for (uint t = start_t; t < end_t; t += C) {
+        barrier();
+        _k[tid] = k[t];
+        _q[tid] = q[t];
+        _g[tid] = g[t];
+        barrier();
+
+        const A_TYPE v_val = v[t];
+        A_TYPE y = 0.0;
+
+        [[unroll]] for (uint i = 0; i < head_size; i += 4) {
+            vec4 k_vec = vec4(_k[i], _k[i+1], _k[i+2], _k[i+3]);
+            vec4 q_vec = vec4(_q[i], _q[i+1], _q[i+2], _q[i+3]);
+            vec4 g_vec = vec4(_g[i], _g[i+1], _g[i+2], _g[i+3]);
+            vec4 s_vec = vec4(state[i], state[i+1], state[i+2], state[i+3]);
+
+            vec4 kv = k_vec * v_val;
+
+            s_vec = s_vec * g_vec + kv;
+            y += dot(q_vec, s_vec);
+
+            state[i]   = s_vec.x;
+            state[i+1] = s_vec.y;
+            state[i+2] = s_vec.z;
+            state[i+3] = s_vec.w;
+        }
+
+        dst[t] = y * scale;
+    }
+
+    [[unroll]] for (uint i = 0; i < head_size; i++) {
+        dst[T * C + batch_id * state_size + head_id * head_size * head_size
+            + i * head_size + tid] = state[i];
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_tq2_0.comp

@@ -0,0 +1,102 @@
+#version 450
+#extension GL_EXT_shader_explicit_arithmetic_types_int32 : require
+
+#include "mul_mat_vec_base.glsl"
+
+layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
+
+FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
+
+// ternary TQ2_0: w = (q - 1) * d. Same qs group/level layout as q2_K, but a
+// single f16 scale per 256-block and no mins:
+//   sum_e b_e * (q_e - 1) * d  =  d * (sum_e b_e * q_e  -  sum_e b_e)
+void calc_superblock(const uint a_offset, const uint b_offset, const uint v_im, const uint q_offset, const uint y_offset, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
+    const uint y_idx = i * QUANT_K + y_offset;
+
+    [[unroll]] for (uint n = 0; n < num_rows; ++n) {
+        const uint ib0 = a_offset + (first_row+n)*num_blocks_per_row;
+        if (i >= num_blocks_per_row) {
+            continue;
+        }
+
+        const uint32_t qs_u32 = uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2]) | (uint32_t(data_a_packed16[ib0 + i].qs[q_offset / 2 + 8]) << 16);
+        const vec4 qs_u32_0 = vec4(unpack8(qs_u32 & 0x03030303));
+        const vec4 qs_u32_2 = vec4(unpack8((qs_u32 >> 2) & 0x03030303));
+        const vec4 qs_u32_4 = vec4(unpack8((qs_u32 >> 4) & 0x03030303));
+        const vec4 qs_u32_6 = vec4(unpack8((qs_u32 >> 6) & 0x03030303));
+
+        const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d);
+
+        [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+            vec2 b0 =   vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  0]);
+            vec2 b16 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 +  8]);
+            vec2 b32 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 16]);
+            vec2 b48 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 24]);
+            vec2 b64 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 32]);
+            vec2 b80 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 40]);
+            vec2 b96 =  vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 48]);
+            vec2 b112 = vec2(data_b_v2[(j*p.batch_stride_b + b_offset + y_idx) / 2 + 56]);
+
+            FLOAT_TYPE sumq = FLOAT_TYPE(0.0);
+            FLOAT_TYPE sumb = FLOAT_TYPE(0.0);
+            [[unroll]] for (int l = 0; l < 2; ++l) {
+                sumq = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(qs_u32_0[l  ]),
+                       fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(qs_u32_0[l+2]),
+                       fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(qs_u32_2[l  ]),
+                       fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(qs_u32_2[l+2]),
+                       fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(qs_u32_4[l  ]),
+                       fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(qs_u32_4[l+2]),
+                       fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(qs_u32_6[l  ]),
+                       fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(qs_u32_6[l+2]), sumq))))))));
+                sumb += FLOAT_TYPE(b0[l]) + FLOAT_TYPE(b16[l]) + FLOAT_TYPE(b32[l]) + FLOAT_TYPE(b48[l])
+                      + FLOAT_TYPE(b64[l]) + FLOAT_TYPE(b80[l]) + FLOAT_TYPE(b96[l]) + FLOAT_TYPE(b112[l]);
+            }
+            temp[j][n] = fma(d, sumq - sumb, temp[j][n]);
+        }
+    }
+}
+
+void compute_outputs(const uint32_t first_row, const uint32_t num_rows) {
+    uint a_offset, b_offset, d_offset;
+    get_offsets(a_offset, b_offset, d_offset);
+
+    const uint num_blocks_per_row = p.ncols / QUANT_K;
+
+    // 16 threads are used to process each block
+    const uint it_size = gl_WorkGroupSize.x/16;
+    const uint tid = gl_LocalInvocationID.x;
+    const uint itid = tid%16;  // 0...15
+    const uint ix = tid/16;
+
+    const uint v_im = itid/8;                                // 0 or 1. 0 computes 0..., 1 computes 128...
+    const uint v_in = itid - 8*v_im;                         // 0...7
+
+    const uint l0 = 2*v_in;                                  // 0...15
+    const uint q_offset = 32*v_im + l0;
+    const uint y_offset = 128*v_im + l0;
+
+    [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
+        [[unroll]] for (uint i = 0; i < NUM_ROWS; ++i) {
+            temp[j][i] = FLOAT_TYPE(0);
+        }
+    }
+
+    for (uint i0 = 0; i0 < num_blocks_per_row; i0 += it_size)
+        calc_superblock(a_offset, b_offset, v_im, q_offset, y_offset, i0 + ix, num_blocks_per_row, first_row, num_rows);
+
+    reduce_result(temp, d_offset, first_row, num_rows, tid);
+}
+
+void main() {
+    const uint first_row = NUM_ROWS * (gl_WorkGroupID.x + gl_NumWorkGroups.x * gl_WorkGroupID.z);
+
+    // do NUM_ROWS at a time, unless there aren't enough remaining rows
+    if (first_row + NUM_ROWS <= p.stride_d) {
+        compute_outputs(first_row, NUM_ROWS);
+    } else {
+        if (first_row >= p.stride_d) {
+            return;
+        }
+        compute_outputs(first_row, p.stride_d - first_row);
+    }
+}

ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp

@@ -119,10 +119,13 @@ layout (constant_id = 3) const uint BK = 16;  // Assumed to be 32 if working wit
 #endif
 
 #ifdef COOPMAT
-#define SHMEM_STRIDE (BK / 2 + 4)
+layout(constant_id = 12) const uint SHMEM_STRIDE_PAD = 4;
+layout(constant_id = 13) const bool APPLY_SLM_A_RESHAPE = false;
 #else
-#define SHMEM_STRIDE (BK / 2 + 1)
+const uint SHMEM_STRIDE_PAD = 1;
+const bool APPLY_SLM_A_RESHAPE = false;
 #endif
+#define SHMEM_STRIDE (BK / 2 + SHMEM_STRIDE_PAD)
 
 shared FLOAT_TYPEV2 buf_a[BM * SHMEM_STRIDE];
 shared FLOAT_TYPEV2 buf_b[BN * SHMEM_STRIDE];
@@ -302,7 +305,7 @@ void main() {
         [[unroll]] for (uint i = 0; i < BK; i += TK) {
             [[unroll]] for (uint cm_row = 0; cm_row < cms_per_row; cm_row++) {
                 // Load from shared into cache
-                coopMatLoad(cache_a, buf_a, (warp_r * WM + cm_row * TM) * SHMEM_STRIDE + i / 2, SHMEM_STRIDE, gl_CooperativeMatrixLayoutRowMajor);
+                coopMatLoad(cache_a, buf_a, a_shmem_index(warp_r * WM + cm_row * TM, i / 2), a_shmem_stride(), gl_CooperativeMatrixLayoutRowMajor);
 
                 [[unroll]] for (uint cm_col = 0; cm_col < cms_per_col; cm_col++) {
                     coopMatLoad(cache_b, buf_b, (warp_c * WN + cm_col * TN) * SHMEM_STRIDE + i / 2, SHMEM_STRIDE, gl_CooperativeMatrixLayoutColumnMajor);